International Journal of Audiology 2014; 53: 318–325
Original Article
Lexical effects on recognition of the NU-6 words by monolingual and bilingual listeners
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Lu-Feng Shi Department of Communication Sciences and Disorders, Long Island University, Brooklyn Campus, New York, USA
Abstract Objective: This study investigated how lexical effects account for word recognition in monolinguals versus bilinguals. Design: Listener-specific error rate and familiarity rating of 200 NU-6 words were obtained. Lexical data (normative familiarity, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) for these words were obtained from the Hoosier mental lexicon. Study sample: Participants included 10 monolinguals and three groups of 10 bilinguals differing mainly in age of acquisition and length of schooling/working in English. Results: Lexical effects were minimal for monolinguals’ word recognition. Listener-specific familiarity rating correlated to error rate better than the Hoosier normative rating. Frequency of occurrence was the most significant lexical variable in accounting for bilinguals’ measures and its effect was the greatest on bilinguals foreign born and educated. Age of English acquisition tended to affect familiarity rating, whereas length of schooling/working in English tended to affect error rate. Conclusions: Frequency of word occurrence significantly affects bilinguals’ familiarity rating and error rate of the NU-6 words. Listener-specific familiarity rating should be obtained to best predict error rate on the test.
Key Words: Bilingual; word recognition; word familiarity; neighborhood activation; frequency of occurrence; neighborhood density; frequency of neighborhood competitors
Word recognition is one important aspect of assessing hearing capacity. As such, it is an indispensible part of routine hearing evaluation and its outcome has multiple clinical implications (Jerger & Jerger, 1981). Words not only conform to acoustic and phonetic constraints, but also contain lexical information that requires knowledge of and experience with language (McArdle & Wilson, 2008). Ever since the pioneering days, the importance of lexical familiarity has always been appreciated by developers of clinical word recognition tests (e.g. Egan, 1948; Howes, 1957; Owen, 1961; Davis, 1978). As the purpose of a word recognition test is to assess the receptive skills, not the linguistic aptitude, of an individual, words included on the test should be highly familiar to the listener in addition to satisfying acoustic and phonetic requirements. Not surprisingly, unfamiliar words result in more errors in recognition than familiar words even when clearly presented (e.g. Howes, 1957; Owens, 1961; Broadbent, 1967; Bradlow & Pisoni, 1999). Consequently, familiar words are more suitable for clinical use than unfamiliar words (e.g. Black, 1952; Walden & Montgomery, 1975; McArdle & Wilson, 2008).
Naturally, familiarity of the test words may present a greater problem for bilingual than native monolingual individuals (Bradlow & Pisoni, 1999; Takayanagi et al, 2002). Some bilinguals may be late learners of the second language and could be in the course of developing their vocabulary. Others may have learned both languages from early childhood, but due to unbalanced exposure and use, eventually favor one over the other language. For these bilinguals, words in the less favored language may not appear as familiar as those in the favored language. Indeed, individual vocabulary size affects bilinguals to a higher degree than monolingual individuals on listening comprehension tasks (Droop & Verhoeven, 2003). Bradlow and Pisoni (1999), for example, compared auditory recognition of lexically “easy” and “hard” English words in both native monolinguals and non-native English learners. Subjective familiarity ratings were obtained using a 7-point scale (1 being the least familiar and 7 being the most, Nusbaum et al, 1984). Native participants gave an average of 6.9 to both easy and hard words, whereas non-natives’ average rating for these two groups of words was 6.6 and 5.1, respectively. The difference in recognition performance between easy and
Correspondence: Lu-Feng Shi, Department of Communication Sciences and Disorders, Long Island University, Brooklyn Campus, Brooklyn, New York 11201, USA. E-mail: [email protected] (Received 23 August 2013; accepted 9 December 2013) ISSN 1499-2027 print/ISSN 1708-8186 online © 2014 British Society of Audiology, International Society of Audiology, and Nordic Audiological Society DOI: 10.3109/14992027.2013.876109
Lexical Effects on Bilingual Word Recognition
Abbreviations
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ANSI NAM NU-6
American National Standards Institute Neighborhood activation model Northwestern University Auditory Test No. 6
hard words was merely 4.3% for natives but 25.2% for non-natives. Such a significant difference was noted by the experimenters to be a possible result of an underdeveloped vocabulary in non-native participants. Moreover, the familiarity rating of hard words was found to correlate significantly to the age at which non-native participants started studying English. Using the same 7-point scale, Shi and Sánchez (2011) obtained familiarity ratings for 200 clinically used monosyllabic English words by two groups of Spanish-English bilinguals who differed in their dominant language. These Northwestern University Auditory Test No. 6 (NU-6) words have been in clinical use for decades (Tillman & Carhart, 1966) and are regarded as easy because they were designed for testing clients with grade-school education. Both groups yielded a high average rating (⬎ 6.5) across all 200 words. Nevertheless, English-dominant bilinguals gave the highest rating (7) to approximately 140 out of 200 words, whereas a rating of 7 was given to fewer than 80 words by Spanish-dominant bilinguals. Familiarity was subsequently found to have a significant influence on word recognition; for both groups of listeners, it accounted for approximately 18% of the total variance in performance (based on the square of the correlation coefficient, ρ2, Cohen, 1988) across test conditions (in quiet and noise). Interestingly, when a cutoff of 6 out of 7 was applied to cull out the least familiar words, the variance in performance accounted by familiarity decreased slightly to approximately 15% in the English-dominant group but remained virtually unchanged in the Spanish-dominant group. When a more stringent cutoff (6.5 out of 7) was applied so that only highly familiar words remained on the test, the influence of familiarity on performance for both groups of bilinguals was further reduced to approximately 8.5%. The findings from Bradlow and Pisoni (1999) and Shi and Sánchez (2011) suggest that word familiarity significantly influences correct recognition in non-native/bilingual individuals and that the language background of a bilingual individual likely mediates the extent of influence of familiarity over her/his word recognition. Data from Shi and Sánchez (2011) further indicate that, from a clinical standpoint, exclusion of words less familiar potentially improves the validity of the test to be used in bilinguals. Subjective familiarity ratings are related to objective measures of lexical characteristics, such as frequency of occurrence, neighborhood density, and frequency of neighborhood competitors (Nusbaum et al, 1984). These characteristics are addressed in the neighborhood activation model (NAM), originally proposed by Luce and Pisoni (1998) to account for relative difficulty of words. A word is considered to be more difficult if it is less frequently used (low frequency of occurrence), has more phonetically similar competitors (high neighborhood density), and/or has competitors that are frequently used (high frequency of neighborhood competitors). As a result, more errors are made or more time is needed in recognition of these difficult words (e.g. Cluff & Luce, 1990; Goldinger et al, 1992; Connine et al, 1993; Vitevitch & Luce, 1999). The NAM has been applied in many previous investigations of word recognition in different native populations (older listeners,
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listeners with hearing loss, cochlear implant users, etc.), based on the words’ frequency of occurrence, neighborhood density, and frequency of neighborhood competitors (Kirk et al, 1995; Sommers & Danielson, 1999; Dirks et al, 2001; McArdle & Wilson, 2008). For normal-hearing listeners, Luce and Pisoni (1998) reported recognition of high-frequency words to be greater by 7.39% than that of words occurring infrequently. Words in a dense neighborhood were recognized less accurately than words in a sparse neighborhood (by 3.38%); however, words with low-frequency competitors in the neighborhood were better recognized than those with high-frequency competitors (by 1.39%). These lexical variables pertain to bilingual word recognition in many different ways. Depending on their degree of proficiency, bilingual listeners could be more or be less affected by the neighborhood density and frequency of the target word. This consideration is included in Luce and Pisoni’s NAM (1998), where matching of the acoustic-phonetic information of an input word to the lexicon in memory is mediated by the frequency and confusability of the input word and the neighborhood words. Stated differently, a bilingual listener’s recognition of English words may be due to difficulty in differentiating the target English word from acoustically and phonetically similar words in the combined lexicon of both languages. Despite the theoretical importance of examining the NAM and related lexical variables in a bilingual population, the model has only been examined in the context of bilingual word recognition in a handful of studies (e.g. Bradlow & Pisoni, 1999; Imai et al, 2005). Bradlow and Pisoni (1999) selected their “easy” and “hard” words based on the NAM characteristics. Their easy words had an average frequency of occurrence of 309.69 per million. By contrast, the average frequency was 12.21 per million for the “hard” words. The density and frequency of the neighborhood competitors were 13.53 and 38.32 for the “easy” words, respectively, and 26.61 and 282.23 for the “hard” words, respectively. The authors reported that non-native listeners better recognized words that had high frequency of occurrence, low density of neighborhood competitors, and low frequencies of competition than those that occurred infrequently, had many of neighborhood competitors, and faced great chances of competition. Imai et al (2005) selected 80 English words that varied in their frequency of occurrence and neighborhood density and recorded them with or without Spanish accent. Fifty-one SpanishEnglish bilinguals listened to these words in quiet. It was found that words with a high neighborhood density (approximately 23.5) were more difficult (by approximately 10%) to recognize than those with a low density (approximately 10.2). The effect of frequency of occurrence was complex in that more high-frequency words (approximately 170 per million) were correctly recognized than low-frequency words (approximately 20 per million) when produced with an accent. Frequency did not play a role for words produced without a Spanish accent. The above two studies lend support to the notion that lexical variables exert certain effects over word recognition in non-native (Bradlow & Pisoni, 1999) or bilingual (Imai et al, 2005) individuals. Findings, however, were not straightforward for several reasons. One reason is that parameters other than lexical variables were part of the study design, such as speech rate in Bradlow and Pisoni (1999) and severity of accent in Imai et al (2005). Thus, findings could reflect mixed effects of lexical variables and rate/accent, rather than lexical effects per se, on bilinguals’ recognition of English speech. Another reason is that words were selected to represent two extremes of each lexical variable, not a wide range of values. Compared to these strategies, correlational analysis including a full scale of lexical variables
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and values may generate new insights of their relative importance to word recognition (Preacher et al, 1978). The current study investigated the lexical aspects (familiarity rating, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) of 200 NU-6 words (Tillman & Carhart, 1966). Correlational analysis was pursued in groups of monolingual and bilingual listeners with varying English learning history. Their recognition and familiarity ratings of the test words were compared to the normative data for the lexical variables from the Hoosier mental lexicon (Nusbaum et al, 1984). The lexicon provided familiarity ratings and reaction time values for 20 000 words included in the Mirriam-Webster Pocket Dictionary. In addition, the experimenters demonstrated that familiarity was related but not equivalent to frequency of occurrence, and advocated that familiarity ratings be included in future studies involving the lexical aspects of words. The lexicon has been since used in many such studies (e.g. Bradlow & Pisoni, 1999; Benkí, 2003; Imai et al, 2005) and was recently employed in an investigation on how lexical information accounted for clinical word recognition in native monolinguals (McArdle & Wilson, 2008). Findings from the current study were expected to extend what was reported by McArdle and Wilson (2008) and help audiologists understand and interpret word recognition performance of their bilingual clients.
Method Participants A total of 40 listeners participated in this study. All listeners had thresholds no greater than 20 dB HL at octave frequencies 250–8000 Hz (ANSI, 2010). Ten were native English monolingual listeners, who served as controls. The average age of these 10 listeners (4 men, 6 women) was 24.5 years old (SD ⫽ 4.7 years). The remaining 30 listeners were bilingual. Their language background information was obtained via the language experience and proficiency questionnaire (LEAP-Q) (Marian et al, 2007). The first group of 10 bilinguals (4 men, 6 women) were born and educated in a bilingual family in the United States. The average age of the group was 24.1 years (SD ⫽ 5.4 years). They spoke Arabic (1), Haitian Creole (1), Hebrew (2), Russian (2), and Spanish (4). Their experience with the English language was supposed to be at or close to a native level; they are hereby referred to as “native” listeners. The second group of 10 bilinguals (4 men, 6 women, average age ⫽ 24.6 years old, SD ⫽ 2.6 years) were born in a country where English is not typically spoken, including Georgia (1), Israel (3), Pakistan (1), the Philippines (1), Russia (1), Ukraine (2), and Uzbekistan (1), but immigrated to the United States in early childhood. Languages spoken by these bilinguals included Georgian (1), Hebrew (2), Russian (3), Tagalog (1), Ukrainian (2), and Urdu (1). This group of bilinguals received formal education in English and was naturalized early in life. For convenience, they are referred to as “intermediate” bilinguals. They were expected to have substantial experience with English via schooling and working, although their proficiency in English might not be as high as their first language (e.g. Shi, 2012, 2013). The last 10 bilinguals (2 men, 8 women, average age ⫽ 26.3 years old, SD ⫽ 6.0 years) came to the United States at a later age, thus learned English late in life, and were still in the process of naturalization during the experiment. They are thus referred to as “non-native” listeners. These bilinguals came from Iran (1), Moldova (1), Pakistan (1), Peru (1), Russia (1), Tajikistan (1), and the Ukraine (4). They spoke Farsi (1), Punjabi (1), Russian (4), Spanish (1), and Ukrainian (3).
Two language background variables, age of English acquisition and length of schooling/working in English, set apart these three bilingual groups (Table 1). Note that distribution of data was greater for non-native bilinguals than other groups, suggesting that non-native bilinguals were more diverse in their English-learning history. Data of age of acquisition failed normality, so Kruskal-Wallis one-way analysis of variance (ANOVA) was thus conducted. Results indicated a significant age of acquisition effect (H2 ⫽ 9.702, p ⫽ 0.008). Tukey’s post hoc comparison indicated that the native group acquired English at a significantly younger age than the intermediate and non-native groups, but the latter two groups did not differ from each other. Data for schooling and working in English passed normality test and one-way ANOVA revealed a significant group effect (F2,27 ⫽ 8.377, p ⫽ 0.001). Tukey’s test indicated that native and intermediate groups were similar for this variable, but both had significantly longer schooling/working experience in English than the non-native group.
Stimuli Two hundred NU-6 words were administered in quiet. The recording used in this study was provided by Dr. Richard Wilson at East Tennessee State University. This recording was selected because it was employed by McArdle and Wilson (2008). At the time the current study was being designed, McArdle and Wilson (2008) was the most recent study that investigated Nusbaum et al’s (1984) lexical variables in the context of a clinical word recognition test. The material was spoken by a professional female talker with a general American dialect and recorded for the Department of Veterans Affairs (McArdle & Wilson, 2008). All words were preceded by the carrier phrase “you will cite…” and followed with a 3.5-s silence. The 200 words were randomized offline. Eight lists of unique word orders (each list included all 200 words) were created and burned onto a compact disc at a sampling rate of 44.1 kHz using iTunes v7.0.2 (Apple Computer, Cupertino, USA).
Procedure One of the eight word lists was randomly selected for each listener. The entire list of 200 words was presented to each listener in one block in quiet at 45 dB HL by the GSI-61 audiometer (GrasonStadler, Madison, USA) and binaurally via a pair of supra-aural headphones (Telephonics, Farmingdale, USA). Word recognition in quiet would likely ascertain lexical effects in a more focused manner than recognition in a competing signal, where results could be influenced by individual acuity of hearing and processing in noise. The presentation level of 45 dB HL was based on a pure-tone average of 5 dB HL at 500, 1000, and 2000 Hz across ears and listeners in the study. This level should be easily audible for normal-hearing listeners and has been consistently used in previous studies involving bilinguals’ Table 1. Age of English acquisition and schooling/working in English for each of the three bilingual listener groups in the form of M ⫾ 1 SD. Shaded cells indicate no statistical difference.
Age of English acquisition (years) Length of schooling/working in English (years)
Native
Intermediate
Non-native
4.10 ⫾ 2.92
8.25 ⫾ 3.63
9.30 ⫾ 5.40
18.19 ⫾ 3.80
16.25 ⫾ 5.85
9.51 ⫾ 5.07
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Lexical Effects on Bilingual Word Recognition recognition of NU-6 words in quiet (e.g. Shi, 2011, 2013). Listeners were instructed to write down and orally report every word they hear. Credit was given if either the written or the oral response was correct. After the listening session was completed, a survey that contained the 200 words was administered on paper (Takayanagi et al, 2002). The word order of this list differed from that of the list used in the listening task. Listeners were asked to read the word and rate the familiarity of the word using a 7-point scale (Nusbaum et al, 1984; Luce & Pisoni, 1998; Bradlow & Pisoni, 1999; Shi & Sánchez, 2011). Three of the seven points were provided in print: (1) “don’t know the word”; (4) “recognize the word, but don’t know the meaning”; and (7) “know the word”, as originally designated by Nusbaum et al (1984). Participants were encouraged to use any number between the anchors including decimals and fractions to describe the familiarity of the word.
Results The rate of errors made on the NU-6 by listener group is illustrated in Figure 1 (upper panel). Visual inspection of the figure indicates highest error rate and lowest familiarity rating with the non-native group.
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Also, the range of data was the greatest with the non-native group for error rate, suggesting that this group of bilinguals was the most heterogeneous, possibly due to their diverse English-learning backgrounds. Error rates passed a normality test (p ⫽ 0.431) and equal variance test (p ⫽ 0.187) and were subsequently submitted to a one-way ANOVA with listener group as the between-subjects factor. Results revealed a significant main effect for listener group (F3,36 ⫽ 18.258, p ⬍ 0.001, η2 ⫽ 0.603). Tukey’s post hoc comparisons showed significantly more errors by the non-native group than monolingual, native, and intermediate groups (p ⬍ 0.001 in all cases), whereas the latter three groups yielded comparable error rates on the test. Listeners’ familiarity ratings averaged across all 200 words are shown in the lower panel of Figure 1. Because these ratings failed normality test (p ⬍ 0.05), a Kruskal-Wallis one-way ANOVA on ranks was performed, which subsequently indicated a significant main effect for listener group (H3 ⫽ 22.121, p ⬍ 0.001, η2 ⫽ 0.763). Tukey’s post hoc comparisons revealed a pattern different from that for error rates; that is, test words were significantly more familiar to monolingual and native than to the non-native group (p ⬍ 0.05 in both cases). The monolingual listeners were also significantly more familiar with the words than the intermediate group (p ⬍ 0.05). The intermediate listeners were not significantly different from either native or non-native listeners. Within-group correlation was computed between listeners’ error rate and familiarity rating. While the entire set of error rate data was normally distributed, within-group data was not. Data for each group failed the Kolmogorov-Smirnov normality test (p ⬍ 0.001 in all cases). Familiarity rating failed the normality test for all groups (p ⬍ 0.001 in all cases) as well. As a result, Spearman’s rank-order correlation was employed to describe the error rate-familiarity rating relationship. The two variables were not correlated for monolingual listeners (ρ ⫽ ⫺ 0.11, p ⫽ 0.121), but significantly correlated for the native (ρ ⫽ ⫺ 0.20, p ⫽ 0.005), intermediate (ρ ⫽ ⫺ 0.18, p ⫽ 0.009), and non-native (ρ ⫽ ⫺ 0.34, p ⬍ 0.001) groups. Correlations for bilinguals were all significant even when Type I error was controlled using Bonferroni correction (α ⫽ 0.05/4 ⫽ 0.0125); however, according to Cohen’s system (1988), correlation had only a small effect for the native and intermediate groups but a medium effect for the non-native group, suggesting that the effect of familiarity was dependent on language background. Hoosier lexical variables include frequency of occurrence, neighborhood density, frequency of neighborhood competitors, and word familiarity. In Table 2, the normative familiarity rating, based on 600 monolingual American college students, is juxtaposed with the rating obtained in the current study by listener group. As one may see, all groups regardless of language background rated most words to be 7, Table 2. Descriptive statistics of familiarity rating for NU-6 words by the Hoosier mental lexicon and the four groups of listeners in the current study. Current study
Figure 1. Participants’ error rate (unfilled circles, upper panel), and familiarity rating (filled circles, lower panel) of NU-6 words. Each symbol represents the median of the group. The error bars represent the 25% and 75% of the data for a given group. Horizontal bars indicate significant pairwise comparisons based on Tukey’s test, where one asterisk indicates significance at p ⬍ 0.05 and three asterisks indicate significance at p ⬍ 0.001.
Hoosier Monolingual Minimum Maximum Median Mean SD
4.25 7 7 6.90 0.32
4.8 7 7 6.98 0.18
Native Intermediate Non-native bilingual bilingual bilingual 4.6 7 7 6.94 0.27
3.4 7 7 6.88 0.44
3.1 7 7 6.82 0.57
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suggesting that these are indeed common words as intended by their developers. A second observation is that the ratings obtained in the monolingual and native groups exceeded the Hoosier norm, whereas those of the intermediate group approached the norm. If ratings were averaged across all three groups, they came fairly close to the Hoosier norm (range: 4.27–7, median ⫽ 7, mean ⫽ 6.94, SD ⫽ 0.30). Spearman’s rank-order correlation revealed significant correlation between the familiarity rating obtained in the current study and the Hoosier familiarity norm for all groups (monolingual: ρ ⫽ 0.30, native: ρ ⫽ 0.34, intermediate: ρ ⫽ 0.46, and non-native: ρ ⫽ 0.38, p ⬍ 0.001 in all cases). These correlation coefficients had a comparably medium effect size, suggesting that group-wise familiarity rating was largely consistent with the Hoosier familiarity norm (i.e. unfamiliar words were rated to be unfamiliar across references). When the Hoosier norm was used to predict error rate, poor correlation was found for monolingual (ρ ⫽ ⫺ 0.12, p ⫽ 0.084), native (ρ ⫽ ⫺ 0.19, p ⫽ 0.013), and intermediate (ρ ⫽ ⫺ 0.15, p ⫽ 0.032) groups. The only significant correlation was found for non-native listeners (ρ ⫽ ⫺ 0.25, p ⬍ 0.001) after Type I error was controlled for (α ⫽ 0.05/4 ⫽ 0.0125). The difference between the data here and those reported earlier (correlation between listeners’ own familiarity rating and error rate) suggested that individual-specific ratings were better than the Hoosier norm, especially for non-native bilinguals. Descriptive statistics for frequency of occurrence, neighborhood density, and neighborhood competitors for the NU-6 words are summarized in Table 3. Because data for these variables are typically distributed on a logarithmic scale, geometric means were computed and are tabulated along with arithmetic means. Indeed, the geometric mean was closer to the median for frequency of occurrence and frequency of competitors. Spearman’s rank-order correlation was computed between each of these lexical variables and listeners’ error rate and familiarity rating to see how much variance these variables might have in common (Table 4). If Bonferroni’s correction for multiple pairwise comparisons was applied (α ⫽ 0.05/24 ⫽ 0.002), only four pairs of significant correlations were found. Three pairs existed between familiarity rating and frequency of occurrence of the target word for the three bilingual groups (ρ increased from 0.30 to 0.43 and 0.48 from native to intermediate and non-native listeners, respectively). One pair was found between error rate and frequency of occurrence for the nonnative group only. These findings suggested that, for less native bilinguals, words less frequently used tended to be less familiar. For
Table 3. Descriptive statistics of frequency of occurrence, neighborhood density, and frequency of neighborhood competitors for NU-6 words by the Hoosier mental lexicon.
Minimum Maximum Median Geometric mean Arithmetic mean
Frequency of occurrence (per million)
Neighborhood density
Frequency of neighborhood competitors (per million)
1 3942 48 41.67 184.45
3 38 19 17.56 19.20
2.33 1527.06 77.45 75.80 152.03
Note: Frequency of occurrence and frequency of neighborhood competitors are based on a corpus of 19 750 words.
Table 4. Spearman’s rank-order correlation between experimental variables by groups in the current study (familiarity rating and error rate) and three lexical variables by the Hoosier mental lexicon (frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) for NU-6 words. Shown in parentheses are p values for the correlation. Frequency of occurrence Monolingual Familiarity rating Error rate Native bilingual Familiarity rating Error rate Intermediate bilingual Familiarity rating Error rate Non-native bilingual Familiarity rating Error rate
0.19 (0.007) ⫺ 0.10 (0.159) 0.30 (⬍ 0.001) ⫺ 0.18 (0.013) 0.43 (⬍ 0.001) ⫺ 0.15 (0.033) 0.48 (⬍ 0.001) ⫺ 0.23 (0.001)
Neighborhood density
Frequency of neighborhood competitors
⫺ 0.04 (0.593) 0.03 (0.671) 0.05 (0.482) ⫺ 0.08 (0.266) 0.01 (0.930) ⫺ 0.06 (0.386) 0.14 (0.061) 0.11 (0.141) 0.052 (0.478) 0.12 (0.095)
0.01 (0.921) 0.06 (0.413)
0.01 (0.860) 0.04 (0.565) 0.10 (0.161) ⫺ 0.05 (0.503)
the least native bilinguals, words less frequently used were also less likely to be correctly recognized.
Discussion It is always of clinical and research interest to understand the contribution of the lexical effects to listener performance on a word recognition test. This interest is enhanced these days by the diversifying language background of the clientele that audiologists service. The current study investigated how lexical variables (word familiarity, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) affect listeners differing in their acquisition of and experience with the English language (monolinguals; “native” bilinguals: born and educated in the United States; “intermediate” bilinguals: foreign born but educated in the United States; and “non-native” bilinguals: foreign born and educated). The average test performance of the monolingual listeners was 94%, lower than the 98%-correct average reported for the Auditec recording of the same NU-6 test (Shi, 2011, 2013). This average score is not unexpected, considering that the Veterans Affairs recording used in this study is slightly more difficult than the Auditec recording (i.e. there is a 5-dB difference in the 50% point between the two psychometric functions, Wilson et al, 1990). McArdle and Wilson (2008) lately conducted a comprehensive investigation on recognition of 490 clinical English words in a group of native listeners. Recognition of words was examined at acoustic (amplitude and duration), phonetic (phoneme features), and lexical (word frequency, neighborhood density, and neighborhood frequency) levels. A minimal lexical effect (merely 3% of the total variance in recognition in noise) was found. This finding is in line with what was observed with the monolingual listeners in the current study, where none of the lexical variables seemed to significantly correlate with performance. The most to which lexical variables may account for performance in quiet was with self-rated familiarity; even so, the amount of variance (ρ2) explained for error rate was 1.21%, 4.00%, and 3.24% for monolingual, native, and intermediate listeners, respectively. These findings suggest that, although there are
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Lexical Effects on Bilingual Word Recognition a few difficult words on the test, they do not substantially preclude a native monolingual or a bilingual individual who has received formal education in an English-spoken school from doing well on the test in quiet. If acoustic and phonetic cues are degraded due to the presence of noise, an enlarged lexical effect may result. Early studies showed word familiarity to be linked to their neighborhood density and frequency of occurrence (e.g. Black, 1952; Howes, 1957; Savin, 1963; Begg & Rowe, 1972; Kreuz, 1987; Dirks et al, 2001). In the current study, listeners’ familiarity rating was not correlated to density or competitor frequency. The test words with the highest neighborhood density include rain, rat, fail, and kill; each has more than 35 competitors that differ from the target word by one phoneme. Nevertheless, these words were all highly familiar to both monolinguals and bilinguals, as each has a familiarity rating of 7 according to the Hoosier norm and received a rating of 7 from all listeners in the current study. By contrast, some words with low neighborhood density (e.g. peg, which has only 10 competitors, or laud, which has 13) were rated to be low in familiarity (6 and 4, respectively, the Hoosier norm; 6.15 and 3.85, averaged across all 40 listeners, the current study). By contrast, the above six example words come from a good range of frequency of occurrence: laud, peg, rat, and fail has a frequency of 1, 4, 6, and 37 per million, respectively, all lower than the median of 48, whereas rain and kill each occurs 80 and 63 per million words, respectively, higher than the median. It thus seems that frequency of occurrence is more representative of word familiarity than neighborhood density. In fact, frequency of occurrence, rather than neighborhood density and frequency of the competitors, was found to be the most significant correlative to bilinguals’ error rate and familiarity rating in the current study. Luce and Pisoni’s NAM (1998) implies that any given acoustic stimulus, word or non-word, will activate a lexicon that shares acoustic and phonetic properties with the stimulus. That is, activation in memory is not simply contingent on the phonetic system of one particular language. Given that bilingual listeners can assimilate English phonemes to the phonetic and phonological system of the other language (e.g. Best et al, 2001), it is not unreasonable to expect all acoustic-phonetically relevant words in both languages to be activated in a given English word’s neighborhood (e.g. Doctor & Klein, 1992; Dijkstra et al, 1999; Specht et al, 2003). Indeed, it has been observed that bilingual listeners relate words from two languages that share similar phonemes and/or graphemes, namely “loanshift” (Romaine, 1995). One example of phonemic loanshift word is kowtow, which originates from Cantonese. Examples of graphemic but not phonemic loanshift widely exit in Japanese kanji (e.g. ), which have a Chinese origin. Examples of loanshift words that share phonemic and graphemic similarities (though not always exactness) across two languages are abundant in English (e.g. sombrero). Including all these cross-language word competitors in error analysis is a daunting task, but may help illustrate the relationship between error rate and lexical variables in future investigations. Furthermore, information of most lexicons comes from visual word recognition; thus, lexical characteristics based on these lexicons may be different from that based on listening experience. Due to the irregular phoneme-grapheme correspondence of the English language, values for some lexical characteristics may differ between visual and auditory stimuli (Jared et al, 1990). For example, the word pint may be considered to be a strong competitor to the word hint if both words are presented visually; however, if presented via the auditory system, the two words are not easily confusable (Balota et al, 2007). This visual-auditory inconsistency makes it difficult to
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estimate how much bilinguals gain their English lexicon, or more specifically, the 200 NU-6 words, through either modality. Bilingual listeners formed three groups with statistically different language backgrounds. Native and intermediate bilingual participants had longer schooling/working in English than non-native participants, but intermediate and non-native participants acquired English later than native participants, suggesting that all three groups had unique background in the development of their English vocabulary. Given that the mental lexicon is expanded over learning and experience (e.g. Plunkett, 1997; Bloom, 2000), differences in performance and familiarity rating would be expected amongst these three bilingual groups. These participants indeed obtained different patterns in their error rate and familiarity rating of the test words. For error rate, intermediate listeners joined native listeners in making significantly fewer errors than non-native listeners. It appears that the NU-6 word recognition test, when administered in quiet, was easy enough to thin down a large amount of bilingual effects. So long as one received formal schooling in an English-speaking country, one could achieve a native or near-native performance level. For familiarity rating, native listeners obtained significantly higher ratings than non-native listeners, whereas intermediate listeners did not significantly differ from either group. Nevertheless, intermediate listeners gave significantly lower average familiarity rating than the monolinguals. Because both intermediate and non-native listeners were foreign born and acquired English significantly later than native listeners, immigrant status and relatively late age of English acquisition apparently contributed to the significantly lower-thanmonolingual familiarity ratings in these two groups of bilinguals. Thus, immersion in a language at a pre-school age has a positive impact on bilinguals’ familiarity with English words. It is known that word acquisition is affected by acquisition age and acquisition channel (through listening versus through reading). Early-acquired words are typically processed faster and more accurately than later-acquired words (e.g. Morrison & Ellis, 1995; Ellis & Morrison, 1998). Words acquired through oral communication are likely to be processed faster and more accurately than words acquired through print (e.g. Gaygen & Luce, 1998; Chateau & Jared, 2000). Intermediate bilinguals, especially those who have longer immersion in an English-speaking community/society, may achieve higher recognition of English words because they acquired more English words through listening at an earlier age than nonnative bilinguals who learned English words predominantly through print. Thus, future studies may benefit from investigating how subjective report of age of word acquisition (Gilhooly & Logie, 1980; Juhasz, 2005) and acquisition channel (Palmeri et al, 1993; Auer & Bernstein, 2008) may account for bilingual listeners’ performance on a typical speech recognition test. There are a few notes concerning the methodology of the current study. Bilinguals’ background was obtained via self-report and their skills in listening, speaking, reading, and writing was not assessed using an actual language achievement test. For the current study, subjective report was appropriate and optimal in soliciting background information such as age of acquisition and length of working/schooling. In addition, subjective report has been successfully employed in many previous studies to obtain bilinguals’ language background (e.g. Marian et al, 2007; Shi & Sánchez, 2010; Shi, 2011, 2012, 2013). Bilinguals of different first languages and nationalities were included in this study rather than bilinguals of the same first language and nationality. The advantage of this design was generalizability of the results to a wide bilingual population. The disadvantage
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was that different first languages might have increased variance in the current findings. This disadvantage might not have posed a serious issue to the integrity of the findings because most of the first languages were phonetically and orthographically distant to English and were comparably distributed across the three bilingual groups. No bilinguals were of a French or Germanic background and could have benefitted from the lexical ties between English and French or between English and other Germanic languages. Spanish does have some graphemic similarity and lexical connections with English. Spanish-English cognates on the NU-6 test include causa-cause and sopa-soup. Another word, date, is also related to the Spanish verb datar. This type of lexical connection however is not expected to benefit Spanish-English bilinguals to a great extent. The NU-6 word recognition test is a listening task. Spanish and English have quite different phonetic systems. It may be argued that Spanish-English bilinguals could have recognized these three words more easily than other bilinguals because familiarity ratings were obtained on words presented in print. Nevertheless, familiarity ratings were invariably 7 across all bilingual groups for these three words. In short, the various first languages should not have significantly confounded the current findings.
Conclusions The current study analysed the error rate and subjective familiarity rating of the NU-6 words, presented in quiet, in four groups of listeners (monolingual; native bilingual: born and educated in the United States; intermediate bilingual: foreign born but educated in the United States; non-native bilingual: foreign born and educated). Results suggest minimal lexical effects on monolingual listeners’ recognition and familiarity rating of the test words. Frequency of occurrence emerged as a significant correlative to bilinguals’ error rate and familiarity rating. Correlation was stronger with familiarity rating than with error rate. The strength of correlation increased, as bilinguals appeared increasingly non-native. In clinical practice, audiologists should expect lexical effects, particularly word familiarity and frequency of occurrence, to play a role in performance on the NU-6 test in most bilingual clients, especially those who are foreign born.
Acknowledgements The author was grateful to all the listeners who participated in this study, Dr. Howard Nusbaum for providing the Hoosier mental lexicon, and Drs. Richard Wilson and Rachel McArdle for providing the recording of the speech material as well as the density and competitor frequency data. Declaration of interest: The author reported no declarations of interest.
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Benkí J.R. 2003. Quantitative evaluation of lexical status, word frequency, and neighborhood density as context effects in spoken word recognition. J Acoust Soc Am, 113, 1689–1705. Best C., McRoberts G. & Goodell E. 2001. Discrimination of non-native consonant contrasts varying in perceptual assimilation to the listener’s native phonological system. J Acoust Soc Am, 109, 775–794. Black J.W. 1952. Accompaniments of word intelligibility. J Speech Hear Disord, 17, 409–418. Bloom P. 2000. How Children Learn the Meanings of Words. Cambridge, USA: MIT Press. Bradlow A.R. & Pisoni D.B. 1999. Recognition of spoken words by native and non-native listeners: Talker-, listener-, and item-related factors. J Acoust Soc Am, 106, 2074–2085. Broadbent D.E. 1967. Word-frequency effect and response bias. Psychol Rev, 74, 1–15. Chateau D. & Jared D. 2000. Exposure to print and word recognition processes. Mem Cogn, 28, 143–153. Cluff M.S. & Luce P.A. 1990. Similarity neighborhoods of spoken bisyllabic words. J Exp Psychol Hum Percept Perform, 16, 551–563. Cohen J. 1988. Statistical Power Analysis for the Behavioral Sciences. Hillside, USA: Lawrence Erlbaum. Connine C.M., Titone D. & Wang J. 1993. Auditory word recognition: Extrinsic and intrinsic effects of word frequency. J Exp Psychol Learn Mem Cogn, 19, 81–94. Davis H. 1978. Audiometry: Pure tone and simple speech tests. In: H. Davis & S.R. Silverman (eds.), Hearing and Deafness. Austin, USA: Holt, Rinehart, and Winston, pp. 183–221. Dijkstra A., Grainger J. & van Heuven W. 1999. Recognition of cognates and interlingual homographs: The neglected role of phonology. J Mem Lang, 41, 496–518. Dirks D.D., Takayanagi S., Moshfegh P., Noffsinger P.D. & Fausti S.A. 2001. Examination of the neighborhood activation theory in normal and hearing-impaired listeners. Ear Hear, 22, 1–13. Doctor E. & Klein D. 1992. Phonological processing bilingual word recognition. In: R. Harris (ed.), Cognitive Processing in Bilinguals. Amsterdam, The Netherlands: Elsevier, pp. 237–252. Droop M. & Verhoeven L. 2003. Language proficiency and reading ability in first-and second-language learners. Read Res Q, 38, 78–103. Egan J. 1948. Articulation testing methods. Laryngoscope, 58, 955–991. Ellis A.W. & Morrison C.M. 1998. Real age-of-acquisition effects in lexical retrieval. J Exp Psychol Learn Mem Cogn, 24, 515–523. Gaygen D.E. & Luce P.A. 1998. Effects of modality on subjective frequency estimates and processing of spoken and printed words. Percept Psychophys, 60, 465–483. Gilhooly K.J. & Logie R.H. 1980. Age-of-acquisition, imagery, concreteness, familiarity and ambiguity measures for 1944 words. Behav Res Methods Instrum, 12, 395–427. Goldinger S.D., Luce P.A., Pisoni B.D. & Marcario J.K. 1992. Formbased priming in spoken word recognition: The roles of competitive activation and response biases. J Exp Psychol Learn Mem Cogn, 18, 1210–1237. Howes D. 1957. On the relation between the intelligibility and frequency of occurrence of English words. J Acoust Soc Am, 29, 296–305. Imai S., Walley A.C. & Flege J.E. 2005. Lexical frequency and neighborhood density effects on the recognition of native and Spanish-accent words by native English and Spanish listeners. J Acoust Soc Am, 117, 896–907. Jared D., McRae K. & Seidenberg M.S. 1990. The basis of consistency effects in word naming. J Mem Lang, 29, 687–715. Jerger S. & Jerger J. 1981. Auditory Disorders – A Manual for Clinical Evaluation. Boston, USA: Little, Brown, and Company. Juhasz B.J. 2005. Age-of-acquisition effects in word and picture identification. Psychol Bull, 131, 684–712. Kirk K.I., Pisoni D.B. & Osberger M.J. 1995. Lexical effects on spoken word recognition by cochlear implant users. Ear Hear, 16, 470–481. Kreuz R.J. 1987. The subjective familiarity of English homophones. Mem Cogn, 15, 154–168.
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Lexical Effects on Bilingual Word Recognition Luce P.A. & Pisoni D.B. 1998. Recognizing spoken words: The neighborhood activation model. Ear Hear, 19, 1–36. Marian V., Blumenfeld H.K. & Kaushanskaya M. 2007. The language experience and proficiency questionnaire (LEAP-Q): Assessing language profiles in bilinguals and multilinguals. J Speech Lang Hear Res, 50, 940–967. McArdle R. & Wilson R.H. 2008. Prediction of word recognition performance in noise by young listeners with normal hearing using acoustic, phonetic, and lexical variables. J Am Acad Audiol, 19, 507–518. Morrison C.M. & Ellis A.W. 1995. Roles of word frequency and age of acquisition in word naming and lexical decision. J Exp Psychol Learn Mem Cogn, 21, 116–133. Nusbaum H.C., Pisoni D.B. & Davis C.K. 1984. Sizing up the Hoosier mental lexicon: Measuring the familiarity of 20 000 words. Research on Speech Perception Progress Report (No. 10). Bloomington: Speech Research Laboratory, Psychology Department, Indiana University. Owens E. 1961. Intelligibility of words varying in familiarity. J Speech Hear Res, 4, 113–129. Palmeri T.J., Goldinger S.D. & Pisoni D.B. 1993. Episodic encoding of voice attributes and recognition memory for spoken words. J Exp Psychol Learn Mem Cogn, 19, 309–328. Plunkett K. 1997. Theories of early language acquisition. Trends Cogn Sci, 1, 146–153. Preacher K.J., Rucker D.D., MacCallum R.C. & Nicewander W.A. 2005. Use of the extreme groups approach: A critical reexamination and new recommendations. Psychol Methods, 10, 178–192. Romaine S. 1995. Bilingualism. Malden: Blackwell. Savin H.B. 1963. Word-frequency effect and errors in the perception of speech. J Acoust Soc Am, 35, 200–206.
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Shi L.-F. 2011. How “proficient” is proficient? Subjective proficiency measure as a predictor of bilingual listeners’ recognition of English words. Am J Audiol, 19, 19–32. Shi L.-F. 2012. Contribution of linguistic variables to bilingual listeners’ perception of degraded English sentences. J Speech Lang Hear Res, 55, 219–234. Shi L.-F. 2013. How “proficient” is proficient? Comparison of English and relative proficiency rating as a predictor of bilingual listeners’ word recognition. Am J Audiol, 21, 40–52. Shi L.-F. & Sánchez D. 2011. The role of word familiarity in Spanish/English bilingual word recognition. Int J Audiol, 50, 66–76. Sommers M.S. & Danielson S.M. 1999. Inhibitory processes and spoken word recognition in young and older adults: The interaction of lexical competition and semantic context. Psychol Aging, 11, 333–341. Specht K., Holtel C., Zahn R., Herzog H., Krause B. et al. 2003. Lexical decision of nonwords and pseudowords in humans: A positron emission tomography study. Neurosci Lett, 345, 177–181. Takayanagi S., Dirks D.D. & Moshfegh A. 2002. Lexical and talker effects on word recognition among native and non-native listeners with normal and impaired hearing. J Speech Lang Hear Res, 45, 585–597. Tillman T.W. & Carhart R. 1966. An expanded test for speech discrimination utilizing CNC monosyllabic words. Northwestern University auditory test no. 6. SAM-TR-66-55, 1–12. Vitevitch M.S. & Luce P.A. 1999. Probablistic phonotactics and neighborhood activation in spoken word recognition. J Mem Lang, 40, 474–408. Wilson R.H., Zizz C.A., Shanks J.E. & Causey J.D. 1990. Normative data in quiet, broadband noise, and competing message for Northwestern University auditory test no. 6 by a female speaker. J Speech Hear Disord, 55, 771–778.
Original Article
Lexical effects on recognition of the NU-6 words by monolingual and bilingual listeners
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Lu-Feng Shi Department of Communication Sciences and Disorders, Long Island University, Brooklyn Campus, New York, USA
Abstract Objective: This study investigated how lexical effects account for word recognition in monolinguals versus bilinguals. Design: Listener-specific error rate and familiarity rating of 200 NU-6 words were obtained. Lexical data (normative familiarity, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) for these words were obtained from the Hoosier mental lexicon. Study sample: Participants included 10 monolinguals and three groups of 10 bilinguals differing mainly in age of acquisition and length of schooling/working in English. Results: Lexical effects were minimal for monolinguals’ word recognition. Listener-specific familiarity rating correlated to error rate better than the Hoosier normative rating. Frequency of occurrence was the most significant lexical variable in accounting for bilinguals’ measures and its effect was the greatest on bilinguals foreign born and educated. Age of English acquisition tended to affect familiarity rating, whereas length of schooling/working in English tended to affect error rate. Conclusions: Frequency of word occurrence significantly affects bilinguals’ familiarity rating and error rate of the NU-6 words. Listener-specific familiarity rating should be obtained to best predict error rate on the test.
Key Words: Bilingual; word recognition; word familiarity; neighborhood activation; frequency of occurrence; neighborhood density; frequency of neighborhood competitors
Word recognition is one important aspect of assessing hearing capacity. As such, it is an indispensible part of routine hearing evaluation and its outcome has multiple clinical implications (Jerger & Jerger, 1981). Words not only conform to acoustic and phonetic constraints, but also contain lexical information that requires knowledge of and experience with language (McArdle & Wilson, 2008). Ever since the pioneering days, the importance of lexical familiarity has always been appreciated by developers of clinical word recognition tests (e.g. Egan, 1948; Howes, 1957; Owen, 1961; Davis, 1978). As the purpose of a word recognition test is to assess the receptive skills, not the linguistic aptitude, of an individual, words included on the test should be highly familiar to the listener in addition to satisfying acoustic and phonetic requirements. Not surprisingly, unfamiliar words result in more errors in recognition than familiar words even when clearly presented (e.g. Howes, 1957; Owens, 1961; Broadbent, 1967; Bradlow & Pisoni, 1999). Consequently, familiar words are more suitable for clinical use than unfamiliar words (e.g. Black, 1952; Walden & Montgomery, 1975; McArdle & Wilson, 2008).
Naturally, familiarity of the test words may present a greater problem for bilingual than native monolingual individuals (Bradlow & Pisoni, 1999; Takayanagi et al, 2002). Some bilinguals may be late learners of the second language and could be in the course of developing their vocabulary. Others may have learned both languages from early childhood, but due to unbalanced exposure and use, eventually favor one over the other language. For these bilinguals, words in the less favored language may not appear as familiar as those in the favored language. Indeed, individual vocabulary size affects bilinguals to a higher degree than monolingual individuals on listening comprehension tasks (Droop & Verhoeven, 2003). Bradlow and Pisoni (1999), for example, compared auditory recognition of lexically “easy” and “hard” English words in both native monolinguals and non-native English learners. Subjective familiarity ratings were obtained using a 7-point scale (1 being the least familiar and 7 being the most, Nusbaum et al, 1984). Native participants gave an average of 6.9 to both easy and hard words, whereas non-natives’ average rating for these two groups of words was 6.6 and 5.1, respectively. The difference in recognition performance between easy and
Correspondence: Lu-Feng Shi, Department of Communication Sciences and Disorders, Long Island University, Brooklyn Campus, Brooklyn, New York 11201, USA. E-mail: [email protected] (Received 23 August 2013; accepted 9 December 2013) ISSN 1499-2027 print/ISSN 1708-8186 online © 2014 British Society of Audiology, International Society of Audiology, and Nordic Audiological Society DOI: 10.3109/14992027.2013.876109
Lexical Effects on Bilingual Word Recognition
Abbreviations
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ANSI NAM NU-6
American National Standards Institute Neighborhood activation model Northwestern University Auditory Test No. 6
hard words was merely 4.3% for natives but 25.2% for non-natives. Such a significant difference was noted by the experimenters to be a possible result of an underdeveloped vocabulary in non-native participants. Moreover, the familiarity rating of hard words was found to correlate significantly to the age at which non-native participants started studying English. Using the same 7-point scale, Shi and Sánchez (2011) obtained familiarity ratings for 200 clinically used monosyllabic English words by two groups of Spanish-English bilinguals who differed in their dominant language. These Northwestern University Auditory Test No. 6 (NU-6) words have been in clinical use for decades (Tillman & Carhart, 1966) and are regarded as easy because they were designed for testing clients with grade-school education. Both groups yielded a high average rating (⬎ 6.5) across all 200 words. Nevertheless, English-dominant bilinguals gave the highest rating (7) to approximately 140 out of 200 words, whereas a rating of 7 was given to fewer than 80 words by Spanish-dominant bilinguals. Familiarity was subsequently found to have a significant influence on word recognition; for both groups of listeners, it accounted for approximately 18% of the total variance in performance (based on the square of the correlation coefficient, ρ2, Cohen, 1988) across test conditions (in quiet and noise). Interestingly, when a cutoff of 6 out of 7 was applied to cull out the least familiar words, the variance in performance accounted by familiarity decreased slightly to approximately 15% in the English-dominant group but remained virtually unchanged in the Spanish-dominant group. When a more stringent cutoff (6.5 out of 7) was applied so that only highly familiar words remained on the test, the influence of familiarity on performance for both groups of bilinguals was further reduced to approximately 8.5%. The findings from Bradlow and Pisoni (1999) and Shi and Sánchez (2011) suggest that word familiarity significantly influences correct recognition in non-native/bilingual individuals and that the language background of a bilingual individual likely mediates the extent of influence of familiarity over her/his word recognition. Data from Shi and Sánchez (2011) further indicate that, from a clinical standpoint, exclusion of words less familiar potentially improves the validity of the test to be used in bilinguals. Subjective familiarity ratings are related to objective measures of lexical characteristics, such as frequency of occurrence, neighborhood density, and frequency of neighborhood competitors (Nusbaum et al, 1984). These characteristics are addressed in the neighborhood activation model (NAM), originally proposed by Luce and Pisoni (1998) to account for relative difficulty of words. A word is considered to be more difficult if it is less frequently used (low frequency of occurrence), has more phonetically similar competitors (high neighborhood density), and/or has competitors that are frequently used (high frequency of neighborhood competitors). As a result, more errors are made or more time is needed in recognition of these difficult words (e.g. Cluff & Luce, 1990; Goldinger et al, 1992; Connine et al, 1993; Vitevitch & Luce, 1999). The NAM has been applied in many previous investigations of word recognition in different native populations (older listeners,
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listeners with hearing loss, cochlear implant users, etc.), based on the words’ frequency of occurrence, neighborhood density, and frequency of neighborhood competitors (Kirk et al, 1995; Sommers & Danielson, 1999; Dirks et al, 2001; McArdle & Wilson, 2008). For normal-hearing listeners, Luce and Pisoni (1998) reported recognition of high-frequency words to be greater by 7.39% than that of words occurring infrequently. Words in a dense neighborhood were recognized less accurately than words in a sparse neighborhood (by 3.38%); however, words with low-frequency competitors in the neighborhood were better recognized than those with high-frequency competitors (by 1.39%). These lexical variables pertain to bilingual word recognition in many different ways. Depending on their degree of proficiency, bilingual listeners could be more or be less affected by the neighborhood density and frequency of the target word. This consideration is included in Luce and Pisoni’s NAM (1998), where matching of the acoustic-phonetic information of an input word to the lexicon in memory is mediated by the frequency and confusability of the input word and the neighborhood words. Stated differently, a bilingual listener’s recognition of English words may be due to difficulty in differentiating the target English word from acoustically and phonetically similar words in the combined lexicon of both languages. Despite the theoretical importance of examining the NAM and related lexical variables in a bilingual population, the model has only been examined in the context of bilingual word recognition in a handful of studies (e.g. Bradlow & Pisoni, 1999; Imai et al, 2005). Bradlow and Pisoni (1999) selected their “easy” and “hard” words based on the NAM characteristics. Their easy words had an average frequency of occurrence of 309.69 per million. By contrast, the average frequency was 12.21 per million for the “hard” words. The density and frequency of the neighborhood competitors were 13.53 and 38.32 for the “easy” words, respectively, and 26.61 and 282.23 for the “hard” words, respectively. The authors reported that non-native listeners better recognized words that had high frequency of occurrence, low density of neighborhood competitors, and low frequencies of competition than those that occurred infrequently, had many of neighborhood competitors, and faced great chances of competition. Imai et al (2005) selected 80 English words that varied in their frequency of occurrence and neighborhood density and recorded them with or without Spanish accent. Fifty-one SpanishEnglish bilinguals listened to these words in quiet. It was found that words with a high neighborhood density (approximately 23.5) were more difficult (by approximately 10%) to recognize than those with a low density (approximately 10.2). The effect of frequency of occurrence was complex in that more high-frequency words (approximately 170 per million) were correctly recognized than low-frequency words (approximately 20 per million) when produced with an accent. Frequency did not play a role for words produced without a Spanish accent. The above two studies lend support to the notion that lexical variables exert certain effects over word recognition in non-native (Bradlow & Pisoni, 1999) or bilingual (Imai et al, 2005) individuals. Findings, however, were not straightforward for several reasons. One reason is that parameters other than lexical variables were part of the study design, such as speech rate in Bradlow and Pisoni (1999) and severity of accent in Imai et al (2005). Thus, findings could reflect mixed effects of lexical variables and rate/accent, rather than lexical effects per se, on bilinguals’ recognition of English speech. Another reason is that words were selected to represent two extremes of each lexical variable, not a wide range of values. Compared to these strategies, correlational analysis including a full scale of lexical variables
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and values may generate new insights of their relative importance to word recognition (Preacher et al, 1978). The current study investigated the lexical aspects (familiarity rating, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) of 200 NU-6 words (Tillman & Carhart, 1966). Correlational analysis was pursued in groups of monolingual and bilingual listeners with varying English learning history. Their recognition and familiarity ratings of the test words were compared to the normative data for the lexical variables from the Hoosier mental lexicon (Nusbaum et al, 1984). The lexicon provided familiarity ratings and reaction time values for 20 000 words included in the Mirriam-Webster Pocket Dictionary. In addition, the experimenters demonstrated that familiarity was related but not equivalent to frequency of occurrence, and advocated that familiarity ratings be included in future studies involving the lexical aspects of words. The lexicon has been since used in many such studies (e.g. Bradlow & Pisoni, 1999; Benkí, 2003; Imai et al, 2005) and was recently employed in an investigation on how lexical information accounted for clinical word recognition in native monolinguals (McArdle & Wilson, 2008). Findings from the current study were expected to extend what was reported by McArdle and Wilson (2008) and help audiologists understand and interpret word recognition performance of their bilingual clients.
Method Participants A total of 40 listeners participated in this study. All listeners had thresholds no greater than 20 dB HL at octave frequencies 250–8000 Hz (ANSI, 2010). Ten were native English monolingual listeners, who served as controls. The average age of these 10 listeners (4 men, 6 women) was 24.5 years old (SD ⫽ 4.7 years). The remaining 30 listeners were bilingual. Their language background information was obtained via the language experience and proficiency questionnaire (LEAP-Q) (Marian et al, 2007). The first group of 10 bilinguals (4 men, 6 women) were born and educated in a bilingual family in the United States. The average age of the group was 24.1 years (SD ⫽ 5.4 years). They spoke Arabic (1), Haitian Creole (1), Hebrew (2), Russian (2), and Spanish (4). Their experience with the English language was supposed to be at or close to a native level; they are hereby referred to as “native” listeners. The second group of 10 bilinguals (4 men, 6 women, average age ⫽ 24.6 years old, SD ⫽ 2.6 years) were born in a country where English is not typically spoken, including Georgia (1), Israel (3), Pakistan (1), the Philippines (1), Russia (1), Ukraine (2), and Uzbekistan (1), but immigrated to the United States in early childhood. Languages spoken by these bilinguals included Georgian (1), Hebrew (2), Russian (3), Tagalog (1), Ukrainian (2), and Urdu (1). This group of bilinguals received formal education in English and was naturalized early in life. For convenience, they are referred to as “intermediate” bilinguals. They were expected to have substantial experience with English via schooling and working, although their proficiency in English might not be as high as their first language (e.g. Shi, 2012, 2013). The last 10 bilinguals (2 men, 8 women, average age ⫽ 26.3 years old, SD ⫽ 6.0 years) came to the United States at a later age, thus learned English late in life, and were still in the process of naturalization during the experiment. They are thus referred to as “non-native” listeners. These bilinguals came from Iran (1), Moldova (1), Pakistan (1), Peru (1), Russia (1), Tajikistan (1), and the Ukraine (4). They spoke Farsi (1), Punjabi (1), Russian (4), Spanish (1), and Ukrainian (3).
Two language background variables, age of English acquisition and length of schooling/working in English, set apart these three bilingual groups (Table 1). Note that distribution of data was greater for non-native bilinguals than other groups, suggesting that non-native bilinguals were more diverse in their English-learning history. Data of age of acquisition failed normality, so Kruskal-Wallis one-way analysis of variance (ANOVA) was thus conducted. Results indicated a significant age of acquisition effect (H2 ⫽ 9.702, p ⫽ 0.008). Tukey’s post hoc comparison indicated that the native group acquired English at a significantly younger age than the intermediate and non-native groups, but the latter two groups did not differ from each other. Data for schooling and working in English passed normality test and one-way ANOVA revealed a significant group effect (F2,27 ⫽ 8.377, p ⫽ 0.001). Tukey’s test indicated that native and intermediate groups were similar for this variable, but both had significantly longer schooling/working experience in English than the non-native group.
Stimuli Two hundred NU-6 words were administered in quiet. The recording used in this study was provided by Dr. Richard Wilson at East Tennessee State University. This recording was selected because it was employed by McArdle and Wilson (2008). At the time the current study was being designed, McArdle and Wilson (2008) was the most recent study that investigated Nusbaum et al’s (1984) lexical variables in the context of a clinical word recognition test. The material was spoken by a professional female talker with a general American dialect and recorded for the Department of Veterans Affairs (McArdle & Wilson, 2008). All words were preceded by the carrier phrase “you will cite…” and followed with a 3.5-s silence. The 200 words were randomized offline. Eight lists of unique word orders (each list included all 200 words) were created and burned onto a compact disc at a sampling rate of 44.1 kHz using iTunes v7.0.2 (Apple Computer, Cupertino, USA).
Procedure One of the eight word lists was randomly selected for each listener. The entire list of 200 words was presented to each listener in one block in quiet at 45 dB HL by the GSI-61 audiometer (GrasonStadler, Madison, USA) and binaurally via a pair of supra-aural headphones (Telephonics, Farmingdale, USA). Word recognition in quiet would likely ascertain lexical effects in a more focused manner than recognition in a competing signal, where results could be influenced by individual acuity of hearing and processing in noise. The presentation level of 45 dB HL was based on a pure-tone average of 5 dB HL at 500, 1000, and 2000 Hz across ears and listeners in the study. This level should be easily audible for normal-hearing listeners and has been consistently used in previous studies involving bilinguals’ Table 1. Age of English acquisition and schooling/working in English for each of the three bilingual listener groups in the form of M ⫾ 1 SD. Shaded cells indicate no statistical difference.
Age of English acquisition (years) Length of schooling/working in English (years)
Native
Intermediate
Non-native
4.10 ⫾ 2.92
8.25 ⫾ 3.63
9.30 ⫾ 5.40
18.19 ⫾ 3.80
16.25 ⫾ 5.85
9.51 ⫾ 5.07
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Lexical Effects on Bilingual Word Recognition recognition of NU-6 words in quiet (e.g. Shi, 2011, 2013). Listeners were instructed to write down and orally report every word they hear. Credit was given if either the written or the oral response was correct. After the listening session was completed, a survey that contained the 200 words was administered on paper (Takayanagi et al, 2002). The word order of this list differed from that of the list used in the listening task. Listeners were asked to read the word and rate the familiarity of the word using a 7-point scale (Nusbaum et al, 1984; Luce & Pisoni, 1998; Bradlow & Pisoni, 1999; Shi & Sánchez, 2011). Three of the seven points were provided in print: (1) “don’t know the word”; (4) “recognize the word, but don’t know the meaning”; and (7) “know the word”, as originally designated by Nusbaum et al (1984). Participants were encouraged to use any number between the anchors including decimals and fractions to describe the familiarity of the word.
Results The rate of errors made on the NU-6 by listener group is illustrated in Figure 1 (upper panel). Visual inspection of the figure indicates highest error rate and lowest familiarity rating with the non-native group.
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Also, the range of data was the greatest with the non-native group for error rate, suggesting that this group of bilinguals was the most heterogeneous, possibly due to their diverse English-learning backgrounds. Error rates passed a normality test (p ⫽ 0.431) and equal variance test (p ⫽ 0.187) and were subsequently submitted to a one-way ANOVA with listener group as the between-subjects factor. Results revealed a significant main effect for listener group (F3,36 ⫽ 18.258, p ⬍ 0.001, η2 ⫽ 0.603). Tukey’s post hoc comparisons showed significantly more errors by the non-native group than monolingual, native, and intermediate groups (p ⬍ 0.001 in all cases), whereas the latter three groups yielded comparable error rates on the test. Listeners’ familiarity ratings averaged across all 200 words are shown in the lower panel of Figure 1. Because these ratings failed normality test (p ⬍ 0.05), a Kruskal-Wallis one-way ANOVA on ranks was performed, which subsequently indicated a significant main effect for listener group (H3 ⫽ 22.121, p ⬍ 0.001, η2 ⫽ 0.763). Tukey’s post hoc comparisons revealed a pattern different from that for error rates; that is, test words were significantly more familiar to monolingual and native than to the non-native group (p ⬍ 0.05 in both cases). The monolingual listeners were also significantly more familiar with the words than the intermediate group (p ⬍ 0.05). The intermediate listeners were not significantly different from either native or non-native listeners. Within-group correlation was computed between listeners’ error rate and familiarity rating. While the entire set of error rate data was normally distributed, within-group data was not. Data for each group failed the Kolmogorov-Smirnov normality test (p ⬍ 0.001 in all cases). Familiarity rating failed the normality test for all groups (p ⬍ 0.001 in all cases) as well. As a result, Spearman’s rank-order correlation was employed to describe the error rate-familiarity rating relationship. The two variables were not correlated for monolingual listeners (ρ ⫽ ⫺ 0.11, p ⫽ 0.121), but significantly correlated for the native (ρ ⫽ ⫺ 0.20, p ⫽ 0.005), intermediate (ρ ⫽ ⫺ 0.18, p ⫽ 0.009), and non-native (ρ ⫽ ⫺ 0.34, p ⬍ 0.001) groups. Correlations for bilinguals were all significant even when Type I error was controlled using Bonferroni correction (α ⫽ 0.05/4 ⫽ 0.0125); however, according to Cohen’s system (1988), correlation had only a small effect for the native and intermediate groups but a medium effect for the non-native group, suggesting that the effect of familiarity was dependent on language background. Hoosier lexical variables include frequency of occurrence, neighborhood density, frequency of neighborhood competitors, and word familiarity. In Table 2, the normative familiarity rating, based on 600 monolingual American college students, is juxtaposed with the rating obtained in the current study by listener group. As one may see, all groups regardless of language background rated most words to be 7, Table 2. Descriptive statistics of familiarity rating for NU-6 words by the Hoosier mental lexicon and the four groups of listeners in the current study. Current study
Figure 1. Participants’ error rate (unfilled circles, upper panel), and familiarity rating (filled circles, lower panel) of NU-6 words. Each symbol represents the median of the group. The error bars represent the 25% and 75% of the data for a given group. Horizontal bars indicate significant pairwise comparisons based on Tukey’s test, where one asterisk indicates significance at p ⬍ 0.05 and three asterisks indicate significance at p ⬍ 0.001.
Hoosier Monolingual Minimum Maximum Median Mean SD
4.25 7 7 6.90 0.32
4.8 7 7 6.98 0.18
Native Intermediate Non-native bilingual bilingual bilingual 4.6 7 7 6.94 0.27
3.4 7 7 6.88 0.44
3.1 7 7 6.82 0.57
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suggesting that these are indeed common words as intended by their developers. A second observation is that the ratings obtained in the monolingual and native groups exceeded the Hoosier norm, whereas those of the intermediate group approached the norm. If ratings were averaged across all three groups, they came fairly close to the Hoosier norm (range: 4.27–7, median ⫽ 7, mean ⫽ 6.94, SD ⫽ 0.30). Spearman’s rank-order correlation revealed significant correlation between the familiarity rating obtained in the current study and the Hoosier familiarity norm for all groups (monolingual: ρ ⫽ 0.30, native: ρ ⫽ 0.34, intermediate: ρ ⫽ 0.46, and non-native: ρ ⫽ 0.38, p ⬍ 0.001 in all cases). These correlation coefficients had a comparably medium effect size, suggesting that group-wise familiarity rating was largely consistent with the Hoosier familiarity norm (i.e. unfamiliar words were rated to be unfamiliar across references). When the Hoosier norm was used to predict error rate, poor correlation was found for monolingual (ρ ⫽ ⫺ 0.12, p ⫽ 0.084), native (ρ ⫽ ⫺ 0.19, p ⫽ 0.013), and intermediate (ρ ⫽ ⫺ 0.15, p ⫽ 0.032) groups. The only significant correlation was found for non-native listeners (ρ ⫽ ⫺ 0.25, p ⬍ 0.001) after Type I error was controlled for (α ⫽ 0.05/4 ⫽ 0.0125). The difference between the data here and those reported earlier (correlation between listeners’ own familiarity rating and error rate) suggested that individual-specific ratings were better than the Hoosier norm, especially for non-native bilinguals. Descriptive statistics for frequency of occurrence, neighborhood density, and neighborhood competitors for the NU-6 words are summarized in Table 3. Because data for these variables are typically distributed on a logarithmic scale, geometric means were computed and are tabulated along with arithmetic means. Indeed, the geometric mean was closer to the median for frequency of occurrence and frequency of competitors. Spearman’s rank-order correlation was computed between each of these lexical variables and listeners’ error rate and familiarity rating to see how much variance these variables might have in common (Table 4). If Bonferroni’s correction for multiple pairwise comparisons was applied (α ⫽ 0.05/24 ⫽ 0.002), only four pairs of significant correlations were found. Three pairs existed between familiarity rating and frequency of occurrence of the target word for the three bilingual groups (ρ increased from 0.30 to 0.43 and 0.48 from native to intermediate and non-native listeners, respectively). One pair was found between error rate and frequency of occurrence for the nonnative group only. These findings suggested that, for less native bilinguals, words less frequently used tended to be less familiar. For
Table 3. Descriptive statistics of frequency of occurrence, neighborhood density, and frequency of neighborhood competitors for NU-6 words by the Hoosier mental lexicon.
Minimum Maximum Median Geometric mean Arithmetic mean
Frequency of occurrence (per million)
Neighborhood density
Frequency of neighborhood competitors (per million)
1 3942 48 41.67 184.45
3 38 19 17.56 19.20
2.33 1527.06 77.45 75.80 152.03
Note: Frequency of occurrence and frequency of neighborhood competitors are based on a corpus of 19 750 words.
Table 4. Spearman’s rank-order correlation between experimental variables by groups in the current study (familiarity rating and error rate) and three lexical variables by the Hoosier mental lexicon (frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) for NU-6 words. Shown in parentheses are p values for the correlation. Frequency of occurrence Monolingual Familiarity rating Error rate Native bilingual Familiarity rating Error rate Intermediate bilingual Familiarity rating Error rate Non-native bilingual Familiarity rating Error rate
0.19 (0.007) ⫺ 0.10 (0.159) 0.30 (⬍ 0.001) ⫺ 0.18 (0.013) 0.43 (⬍ 0.001) ⫺ 0.15 (0.033) 0.48 (⬍ 0.001) ⫺ 0.23 (0.001)
Neighborhood density
Frequency of neighborhood competitors
⫺ 0.04 (0.593) 0.03 (0.671) 0.05 (0.482) ⫺ 0.08 (0.266) 0.01 (0.930) ⫺ 0.06 (0.386) 0.14 (0.061) 0.11 (0.141) 0.052 (0.478) 0.12 (0.095)
0.01 (0.921) 0.06 (0.413)
0.01 (0.860) 0.04 (0.565) 0.10 (0.161) ⫺ 0.05 (0.503)
the least native bilinguals, words less frequently used were also less likely to be correctly recognized.
Discussion It is always of clinical and research interest to understand the contribution of the lexical effects to listener performance on a word recognition test. This interest is enhanced these days by the diversifying language background of the clientele that audiologists service. The current study investigated how lexical variables (word familiarity, frequency of occurrence, neighborhood density, and frequency of neighborhood competitors) affect listeners differing in their acquisition of and experience with the English language (monolinguals; “native” bilinguals: born and educated in the United States; “intermediate” bilinguals: foreign born but educated in the United States; and “non-native” bilinguals: foreign born and educated). The average test performance of the monolingual listeners was 94%, lower than the 98%-correct average reported for the Auditec recording of the same NU-6 test (Shi, 2011, 2013). This average score is not unexpected, considering that the Veterans Affairs recording used in this study is slightly more difficult than the Auditec recording (i.e. there is a 5-dB difference in the 50% point between the two psychometric functions, Wilson et al, 1990). McArdle and Wilson (2008) lately conducted a comprehensive investigation on recognition of 490 clinical English words in a group of native listeners. Recognition of words was examined at acoustic (amplitude and duration), phonetic (phoneme features), and lexical (word frequency, neighborhood density, and neighborhood frequency) levels. A minimal lexical effect (merely 3% of the total variance in recognition in noise) was found. This finding is in line with what was observed with the monolingual listeners in the current study, where none of the lexical variables seemed to significantly correlate with performance. The most to which lexical variables may account for performance in quiet was with self-rated familiarity; even so, the amount of variance (ρ2) explained for error rate was 1.21%, 4.00%, and 3.24% for monolingual, native, and intermediate listeners, respectively. These findings suggest that, although there are
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Lexical Effects on Bilingual Word Recognition a few difficult words on the test, they do not substantially preclude a native monolingual or a bilingual individual who has received formal education in an English-spoken school from doing well on the test in quiet. If acoustic and phonetic cues are degraded due to the presence of noise, an enlarged lexical effect may result. Early studies showed word familiarity to be linked to their neighborhood density and frequency of occurrence (e.g. Black, 1952; Howes, 1957; Savin, 1963; Begg & Rowe, 1972; Kreuz, 1987; Dirks et al, 2001). In the current study, listeners’ familiarity rating was not correlated to density or competitor frequency. The test words with the highest neighborhood density include rain, rat, fail, and kill; each has more than 35 competitors that differ from the target word by one phoneme. Nevertheless, these words were all highly familiar to both monolinguals and bilinguals, as each has a familiarity rating of 7 according to the Hoosier norm and received a rating of 7 from all listeners in the current study. By contrast, some words with low neighborhood density (e.g. peg, which has only 10 competitors, or laud, which has 13) were rated to be low in familiarity (6 and 4, respectively, the Hoosier norm; 6.15 and 3.85, averaged across all 40 listeners, the current study). By contrast, the above six example words come from a good range of frequency of occurrence: laud, peg, rat, and fail has a frequency of 1, 4, 6, and 37 per million, respectively, all lower than the median of 48, whereas rain and kill each occurs 80 and 63 per million words, respectively, higher than the median. It thus seems that frequency of occurrence is more representative of word familiarity than neighborhood density. In fact, frequency of occurrence, rather than neighborhood density and frequency of the competitors, was found to be the most significant correlative to bilinguals’ error rate and familiarity rating in the current study. Luce and Pisoni’s NAM (1998) implies that any given acoustic stimulus, word or non-word, will activate a lexicon that shares acoustic and phonetic properties with the stimulus. That is, activation in memory is not simply contingent on the phonetic system of one particular language. Given that bilingual listeners can assimilate English phonemes to the phonetic and phonological system of the other language (e.g. Best et al, 2001), it is not unreasonable to expect all acoustic-phonetically relevant words in both languages to be activated in a given English word’s neighborhood (e.g. Doctor & Klein, 1992; Dijkstra et al, 1999; Specht et al, 2003). Indeed, it has been observed that bilingual listeners relate words from two languages that share similar phonemes and/or graphemes, namely “loanshift” (Romaine, 1995). One example of phonemic loanshift word is kowtow, which originates from Cantonese. Examples of graphemic but not phonemic loanshift widely exit in Japanese kanji (e.g. ), which have a Chinese origin. Examples of loanshift words that share phonemic and graphemic similarities (though not always exactness) across two languages are abundant in English (e.g. sombrero). Including all these cross-language word competitors in error analysis is a daunting task, but may help illustrate the relationship between error rate and lexical variables in future investigations. Furthermore, information of most lexicons comes from visual word recognition; thus, lexical characteristics based on these lexicons may be different from that based on listening experience. Due to the irregular phoneme-grapheme correspondence of the English language, values for some lexical characteristics may differ between visual and auditory stimuli (Jared et al, 1990). For example, the word pint may be considered to be a strong competitor to the word hint if both words are presented visually; however, if presented via the auditory system, the two words are not easily confusable (Balota et al, 2007). This visual-auditory inconsistency makes it difficult to
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estimate how much bilinguals gain their English lexicon, or more specifically, the 200 NU-6 words, through either modality. Bilingual listeners formed three groups with statistically different language backgrounds. Native and intermediate bilingual participants had longer schooling/working in English than non-native participants, but intermediate and non-native participants acquired English later than native participants, suggesting that all three groups had unique background in the development of their English vocabulary. Given that the mental lexicon is expanded over learning and experience (e.g. Plunkett, 1997; Bloom, 2000), differences in performance and familiarity rating would be expected amongst these three bilingual groups. These participants indeed obtained different patterns in their error rate and familiarity rating of the test words. For error rate, intermediate listeners joined native listeners in making significantly fewer errors than non-native listeners. It appears that the NU-6 word recognition test, when administered in quiet, was easy enough to thin down a large amount of bilingual effects. So long as one received formal schooling in an English-speaking country, one could achieve a native or near-native performance level. For familiarity rating, native listeners obtained significantly higher ratings than non-native listeners, whereas intermediate listeners did not significantly differ from either group. Nevertheless, intermediate listeners gave significantly lower average familiarity rating than the monolinguals. Because both intermediate and non-native listeners were foreign born and acquired English significantly later than native listeners, immigrant status and relatively late age of English acquisition apparently contributed to the significantly lower-thanmonolingual familiarity ratings in these two groups of bilinguals. Thus, immersion in a language at a pre-school age has a positive impact on bilinguals’ familiarity with English words. It is known that word acquisition is affected by acquisition age and acquisition channel (through listening versus through reading). Early-acquired words are typically processed faster and more accurately than later-acquired words (e.g. Morrison & Ellis, 1995; Ellis & Morrison, 1998). Words acquired through oral communication are likely to be processed faster and more accurately than words acquired through print (e.g. Gaygen & Luce, 1998; Chateau & Jared, 2000). Intermediate bilinguals, especially those who have longer immersion in an English-speaking community/society, may achieve higher recognition of English words because they acquired more English words through listening at an earlier age than nonnative bilinguals who learned English words predominantly through print. Thus, future studies may benefit from investigating how subjective report of age of word acquisition (Gilhooly & Logie, 1980; Juhasz, 2005) and acquisition channel (Palmeri et al, 1993; Auer & Bernstein, 2008) may account for bilingual listeners’ performance on a typical speech recognition test. There are a few notes concerning the methodology of the current study. Bilinguals’ background was obtained via self-report and their skills in listening, speaking, reading, and writing was not assessed using an actual language achievement test. For the current study, subjective report was appropriate and optimal in soliciting background information such as age of acquisition and length of working/schooling. In addition, subjective report has been successfully employed in many previous studies to obtain bilinguals’ language background (e.g. Marian et al, 2007; Shi & Sánchez, 2010; Shi, 2011, 2012, 2013). Bilinguals of different first languages and nationalities were included in this study rather than bilinguals of the same first language and nationality. The advantage of this design was generalizability of the results to a wide bilingual population. The disadvantage
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was that different first languages might have increased variance in the current findings. This disadvantage might not have posed a serious issue to the integrity of the findings because most of the first languages were phonetically and orthographically distant to English and were comparably distributed across the three bilingual groups. No bilinguals were of a French or Germanic background and could have benefitted from the lexical ties between English and French or between English and other Germanic languages. Spanish does have some graphemic similarity and lexical connections with English. Spanish-English cognates on the NU-6 test include causa-cause and sopa-soup. Another word, date, is also related to the Spanish verb datar. This type of lexical connection however is not expected to benefit Spanish-English bilinguals to a great extent. The NU-6 word recognition test is a listening task. Spanish and English have quite different phonetic systems. It may be argued that Spanish-English bilinguals could have recognized these three words more easily than other bilinguals because familiarity ratings were obtained on words presented in print. Nevertheless, familiarity ratings were invariably 7 across all bilingual groups for these three words. In short, the various first languages should not have significantly confounded the current findings.
Conclusions The current study analysed the error rate and subjective familiarity rating of the NU-6 words, presented in quiet, in four groups of listeners (monolingual; native bilingual: born and educated in the United States; intermediate bilingual: foreign born but educated in the United States; non-native bilingual: foreign born and educated). Results suggest minimal lexical effects on monolingual listeners’ recognition and familiarity rating of the test words. Frequency of occurrence emerged as a significant correlative to bilinguals’ error rate and familiarity rating. Correlation was stronger with familiarity rating than with error rate. The strength of correlation increased, as bilinguals appeared increasingly non-native. In clinical practice, audiologists should expect lexical effects, particularly word familiarity and frequency of occurrence, to play a role in performance on the NU-6 test in most bilingual clients, especially those who are foreign born.
Acknowledgements The author was grateful to all the listeners who participated in this study, Dr. Howard Nusbaum for providing the Hoosier mental lexicon, and Drs. Richard Wilson and Rachel McArdle for providing the recording of the speech material as well as the density and competitor frequency data. Declaration of interest: The author reported no declarations of interest.
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