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Contact author: Amy T. Neel, Department of Speech and Hearing Sciences, University of New Mexico, Albuquerque, NM 87131. E-mail: atneel{at}unm.edu.
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Method: Acoustic characteristics of 10 vowels produced by 45 men and 48 women from the J. M. Hillenbrand, L. A. Getty, M. J. Clark, and K. Wheeler (1995) study were examined and compared with identification accuracy. Global (mean f0, F1, and F2; duration; and amount of formant movement) and fine-grained measures (vowel space area; mean distance among vowels; f0, F1, and F2 ranges; duration ratio between long and short vowels; and formant movement ratio between dynamic and static vowels) were used to predict identification scores. Acoustic measures of the most frequently confused pairs (/æ/–/
/ and /
/–/
/) were compared.
Results: Global and fine-grained measures accounted for less than 1/4 of variance in identification scores: Vowel space area alone accounted for 9%–12% of variance. Differences in vowel identification were largely due to poor identification of /æ/, //, //, or //. Well-identified vowels were distinctive in formant frequencies, duration, and amount of formant movement over time.
Conclusions: Distinctiveness among neighboring vowels is more important in determining vowel intelligibility than vowel space area. Acoustic comparison of confused vowels may be more useful in studying intelligibility of normal and disordered speech than in measuring vowel space area.
KEY WORDS: vowels, speech intelligibility, speech perception
Vowel formant frequency values and vowel space measures based on them have been widely used in the study of speech to assess the impact on speech of various disorders such as stuttering (e.g., Prosek, Montgomery, Walden, & Hawkins, 1987) and dysarthria (e.g., Turner, Tjaden, & Weismer, 1995), to detect changes in speech perception and production with cochlear implants (Lane, Matthies, Perkell, Vick, & Zandipour, 2001), in cross-language comparisons (e.g., Bradlow, 1995), and to assess speech intelligibility (e.g., Bradlow, Torretta, & Pisoni, 1996). Most studies use the measure of vowel quality originally developed by Peterson and Barney (1952), the values of the first two or three formant frequencies taken from the steady-state portion of the vowel. Interspeaker differences in temporal and spectral characteristics are well known: Differences in duration, fundamental frequency, and formant frequency values have been found even for speakers of the same age, gender, and dialect (Hillenbrand, Getty, Clark, & Wheeler, 1995; Peterson & Barney, 1952). The purpose of this study was to determine the usefulness of vowel production characteristics in predicting vowel identification scores, one measure of speech intelligibility, for a large group of adults with normal speech.
Some characteristics of vowel production have been associated with overall speech intelligibility. Bond and Moore (1994) assessed word and sentence intelligibility for 5 young male talkers. They found that the talker with the least intelligible words and sentences had the shortest vowel durations and the smallest vowel space. Bradlow et al. (1996), in studying the intelligibility of sentences produced by 10 male and 10 female talkers, found that vowel space dispersion (the spacing of vowels in the F1 x F2 plane) and F1 range were significantly correlated with overall sentence intelligibility. Hazan and Markham (2004), in studying British English, found that F2 differences between /i/ and /u/ were significantly correlated with word intelligibility.
Clear speech studies have provided some information about the acoustic characteristics of highly identifiable vowels in a small number of normal talkers. Vowels produced in a clear, carefully articulated manner are longer in duration than vowels produced in conversational style speech, and clear vowels occupy a larger area in the F1 x F2 space than conversational vowels (Ferguson & Kewley-Port, 2002; Picheny, Durlach, & Braida, 1986). Ferguson and Kewley-Port (2002) reported that clear vowels produced by a single male talker had higher F1 values than conversational vowels and that F2 values for front vowels were generally higher and F2 values for back vowels generally lower than those produced in conversational style. They also found that some of the clear vowels produced by a single male talker had more dynamic formant trajectories over the course of the phoneme than conversational vowels.
Studies of disordered speech have also provided information about the association between acoustic vowel characteristics and intelligibility. Vowel space area, the area within the quadrilateral formed by the corner vowels /i/, /æ/, //, and /u/, has been used in a number of recent studies as an index of articulatory working space and speech intelligibility. The assumption of these studies is that larger vowel space areas indicate greater excursions of the articulators in terms of tongue height (F1 dimension) or tongue advancement (F2 dimension). It is presumed that speech intelligibility is impaired because speech disorders are characterized by reductions in articulatory working space. These investigations have documented reduced vowel space area in speech disorders ranging from dysarthria in adults (e.g., Bunton, 2006; McRae, Tjaden, & Schoonings, 2002; Tjaden & Wilding, 2004; Turner et al., 1995; Weismer, Jeng, Laures, Kent, & Kent, 2001) and children (Higgins & Hodge, 2002; Liu, Tsao, & Kuhl, 2005) to hearing-impaired individuals (Palethorpe & Watson, 2003), speakers who have undergone glossectomy (Whitehill, Ciocca, Chan, & Samman, 2006), and boys with Fragile X syndrome (Zajac et al., 2006). Studies of disordered speech, however, have varied widely in the predictive value of vowel space area for speech intelligibility. Tjaden and Wilding (2004) found that vowel space area accounted for only 6%–8% of variance in intelligibility ratings for females with Parkinson disease and multiple sclerosis. For speakers with Parkinson disease, McRae et al. (2002) showed that vowel space area accounted for 13% of variance in sentence intelligibility ratings. Both Turner et al. (1995) and Weismer et al. (2001) reported that vowel space area accounted for about 45% of variance in intelligibility scores for speakers with dysarthria related to amyotrophic lateral sclerosis (ALS). Higgins and Hodge (2002) reported that vowel space area predicted 64% of variance in sentence intelligibility for children with dysarthria.
Despite numerous studies relating vowel space area to speech intelligibility, there is little research focused specifically on the relation between vowel identification scores and vowel space area. Liu et al. (2005) studied the relation between vowel space area and vowel intelligibility in Mandarin-speaking males with cerebral palsy. They found a significant correlation between vowel space area and intelligibility for the three vowels /i/, //, and /u/ (R2 = .63). Similarly, Whitehill et al. (2006) found a significant correlation in vowel space area for the four vowels /i/, /e/, //, and /u/ and vowel intelligibility in Cantonese speakers with partial glossectomy (R2 = .32). Three studies have demonstrated improved identification scores or goodness ratings and increased vowel space areas for at least some speakers with Parkinson disease when they use loud speech techniques (Bunton, 2006; Neel & Beveridge, 2006; Spielman, Ramig, & Fox, 2005). However, there is no information on the relation between vowel space area and vowel identification scores for speakers of languages with relatively crowded vowel spaces such as English.
The aim of this study was to determine if acoustic characteristics of vowels predict vowel identification scores from listeners. Several global measures of vowel production were motivated by the clear speech findings. If talkers lengthen vowels, increase formant dynamics, and change formant frequencies for more intelligible, carefully articulated speech compared with conversational speech, it is possible that speakers who produce longer, more dynamic vowels on average may receive higher vowel identification scores than speakers with shorter, less dynamic vowels. Thus, the five global acoustic characteristics included mean fundamental frequency, mean F1 and F2 frequencies, mean duration, and mean amount of formant movement across the 10 vowel sets produced by each talker. In addition, a set of fine-grained or distinctive vowel characteristics was developed based on the intelligibility literature to assess the ways in which speakers can differentiate among vowels in the crowded F1 x F2 space of American English. This set included measures of vowel space area and dispersion; ranges for f0, F1, and F2; duration ratios between long and short vowels; and formant movement ratios between vowels with relatively great formant movement and those with little formant movement over the time course of the vowel.
Method
Material
This study used vowel identification data and acoustic measures obtained by Hillenbrand and his colleagues for their replication and extension of the classic Peterson and Barney (1952) study. The data sets were downloaded from Hillenbrand's Web site (http://homepages.wmich.edu/~hillenbr/voweldata.html). Hillenbrand et al. (1995) recorded 12 vowels produced in /hVd/ context by 45 men and 48 women from the Michigan/Upper Midwest dialect of American English. The acoustic analysis of these vowels included vowel duration, fundamental frequency, and formant frequencies F1, F2, and F3 at the steady-state portion of the vowel and at 10% intervals throughout the vowel. They also reported identification data from 20 listeners from the same dialect as the talkers. Information about the recordings and acoustic analysis techniques is available in Hillenbrand et al.
The original Hillenbrand et al. (1995) database included both // and /
/. Because the distinction between // and // may not be maintained even in this Midwestern dialect, the vowel // was eliminated from analysis in the present study. Any responses of // for // were scored as correct // responses. In addition, the vowel /
/ was eliminated from the database because this study focused on vowels that can be distinguished in the F1 x F2 space. Thus, the 10 vowels included in this study are /i, I, e, , æ, , o, 
, , u/.
Acoustic Measures
Two approaches to quantifying vowel space were explored in this study. The first set of acoustic measures focused on describing the mean characteristics of the entire vowel set. For each talker, five global vowel space measures were calculated. Mean f0 was obtained by averaging the steady-state f0 values in Hz across the 10 vowels. For mean F1 and mean F2, F1 and F2 steady-state values for the 10 vowels were transformed into Bark units (Traunmuller, 1990) and averaged. Mean duration consisted of the average duration values in milliseconds across the 10 vowels. Mean amount of formant movement was used to ascertain the dynamic nature of each talker's vowels. For each vowel, the Euclidean distance in the F1 x F2 bark space from the vowel onset (20% of vowel duration) to the steady state (see Hillenbrand et al., 1995, for description) and the Euclidean distance from the vowel steady state to the offset (80% of vowel duration) were calculated and summed. These distances were then averaged across the 10 vowels for each talker. Distances were calculated using these three points in the vowel trajectory through the F1 x F2 space because listeners benefit from acoustic cues at onset, midpoint or steady-state, and offset positions (Neel, 2004).
A second set of seven fine-grained measures focused on characterizing the distinctiveness among each talker's vowels. For vowel space area, Heron's formula (Weisstein, 2003) was used to calculate the area of the irregular quadrilateral using two triangles in the F1 x F2 bark space. To compute the area of a triangle given lengths of the three sides a, b, and c, first the semiperimeter is calculated using the formula s = 
(a + b + c). The area can then be calculated by taking the square root of s(s–a)(s–b)(s–c). The first triangle consisted of the Euclidean distances from /i/ to /æ/, /æ/ to /u/, and /u/ to /i/, and the second triangle consisted of the Euclidean distances from /æ/ to /u/, /u/ to //, and // to /æ/. The areas of the two triangles are summed to determine the area of the vowel quadrilateral formed by the "corner" vowels /i, æ, , u/. Mean distance among vowels was used to assess the dispersion of vowels within the F1 x F2 vowel space; it was calculated by obtaining the Euclidean distance between each pair of the 10 vowels and averaging those values. In order to weigh the contributions of F1 and F2 to vowel space area separately, F1 range and F2 range were calculated by subtracting the lowest F1 (or F2) value from the highest F1 (or F2) value in Bark units. Lowest and highest values of F1 and F2 were not restricted to the corner vowels. F0 range was calculated by subtracting the lowest f0 value across the 10 vowels from the highest value in Hz. Duration ratio was used to obtain an estimate of distinctiveness in vowel length. The vowels /I, , , / had short durations (male M = 204 ms, female M = 258 ms) and the vowels /, o, e, æ/ had long durations (male M = 266 ms, female M = 327 ms). For each talker, the average value of the four long vowels was divided by the average value of the four short vowels. The vowels /i/ and /u/ had intermediate values as found by Jenkins, Strange, and Miranda (1993) and were not included in the calculation of duration ratio. Dynamic ratio was used to assess distinctiveness among vowels with relatively dynamic and relatively static trajectories. Mean Euclidean distances from vowel onsets to steady states to offsets in the F1 x F2 bark space for each vowel were averaged across the male and female talkers. For both groups of talkers, the most dynamic vowels were /æ, , / (male M = 2.20, female M = 3.29), and the least dynamic vowels were /i, , u/ (male M = 0.72, female M = 1.00). The vowels /I, e, o, / had intermediate values for this /hVd/ context and were not included in the dynamic ratio. The dynamic ratio for each talker consisted of the average Euclidean distance traveled by the three most dynamic vowels divided by the average distance traversed by the three most static vowels. All statistical analyses were conducted using STATISTICA (StatSoft, 2003).
Results
Vowel Identification Scores
Percent listener-correct scores across the 10 vowels for each talker were converted into rationalized arcsine units (RAUs) prior to statistical analyses (Studebaker, 1985). Hillenbrand et al. (1995), using the full 12-vowel sets produced by men, women, and children, reported a significant but small identification advantage for women, but the identification scores for men and women for the 10-vowel set used in this analysis did not significantly differ (t = 1.70, p > .09). The mean identification score for men was 95.6% (SD = 4.0%) and for women was 96.8% (SD = 2.6). Scores ranged from 78% correct for talker Male 41 to 100% correct for talkers Male 29, Male 39, and Female 34 . Identification rates were skewed toward ceiling values: Only 12 men and 10 women had scores below 95% correct.
Vowel Space Measures
Mean values of the global vowel measures for male and female talkers are shown in Tables 1 and 2. As reported by Hillenbrand et al. (1995), women had significantly higher mean f0, mean F1, and mean F2 values and had longer vowels on average than men (t = 6.80, p < .01). In addition, the women produced more dynamic formant trajectories in the F1 x F2 space than men (mean amount of formant movement: t = 8.43, p < .01).
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/, and /u/ across each group of talkers. The vowel space for the talker in each group with the smallest and largest vowel spaces is also shown.
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/, and /u/ used to calculate vowel space area did not represent the most extreme values for many talkers. For 92% of talkers, /a/ represented the highest F1 value, but /i/ was the lowest F1 value for only 69% of talkers. The vowel /i/ was the highest F2 value for 99% of talkers, but /u/ represented the lowest F2 for only 32% of talkers. For most of the talkers (65%), the lowest static F2 value was obtained for /o/.
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Prediction of Identification Scores From Global and Fine-Grained Measures
To determine which of the vowel production measures predict identification scores, the five global measures and eight fine-grained measures were submitted to multiple regression analyses. An all-possible subsets analysis was conducted to determine which variables from the global and fine-grained sets accounted for the most variance in identification scores (Neter, Wasserman, and Kutner, 1985). Identification scores for each talker constituted the dependent variable. Separate analyses were conducted for male and female talkers. Adjusted R2 rather than R2 was used to evaluate the proportion of variance accounted for by each subset to minimize increasing R2 by chance as more variables were added.
No single factors or sets of factors among the global measures significantly predicted identification scores for the male talkers, whereas for the female talkers, only the mean amount of formant movement significantly predicted vowel identification accuracy (adjusted R2 = .11, p < .02). Fine-grained measures (shown in Table 5) did account for a significant but small proportion of variance in identification scores for both sets of talkers. For the males, the best subset included vowel space area, F2 range, and duration ratio (adjusted R2 = .16, p < .02). Vowel space area alone predicted about 12% of variance in male identification scores (p < .02), and F1 range alone predicted about 14% of variance in scores (p < .02). The best subset for the females consisted of vowel space area, f0 range, duration ratio, and dynamic ratio (adjusted R2 = .18, p < .02). Vowel space area alone predicted only 9% of variance in female identification scores (p < .02).
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Confusion Matrices
One reason for the failure of vowel space characteristics to account for much variance in overall vowel identification scores becomes apparent when the confusion matrices (shown in Tables 6 and 7) are examined. Errors in identification are not spread evenly across the 10 vowels. There are only 3 or 4 vowels for which accuracy rates fall below 97% across all the talkers: //, /æ/, and // for both groups and // for the males. These four vowels account for 78% of errors made by listeners for the male and female talkers combined. The confusions nearly always occurred between spectral neighbors: // and /æ/ were confused for one another, and // and // were confused with each other. The confusions were not symmetrical nor were they spread evenly across talkers.
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/, //, and // were examined. Because the stimuli were presented by Hillenbrand et al. (1995) in random order and not blocked by talker, within-talker comparisons of vowel pairs were not warranted. Instead, identification scores and acoustic characteristics were submitted to discriminant analysis in STATISTICA to ascertain which characteristics would best classify well-identified tokens (100% accurate) and poorly identified tokens (at or below 85% accuracy). Figures 3 and 4 contain dynamic F1 x F2 plots for the mean well-identified and poorly identified vowel pairs. The figures show F1 x F2 positions for each vowel at onset (20% of vowel duration), steady state, and offset (80% of vowel duration), with the larger symbols representing the onset position.
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/, discriminant analysis was not performed for this vowel.
For the male // vowels, the 28 well-identified vowels and 7 poorly identified vowels were classified with 94% accuracy by a model containing f0, F1 at onset, F2 at steady state, F2 change from onset to offset, and duration (Wilks' 
= .38, p < .01). Examination of means revealed that well-identified vowels on average were characterized by lower values for f0, F1, and F2, less F2 movement over the course of the vowel, and shorter duration than the poorly identified versions. For the female // vowels, overall classification accuracy for the 26 well-identified and 11 poorly identified tokens was 76%: Poorly identified tokens were classified with only 36% accuracy (Wilks' = .87, p < .04). The well-identified // vowels had less dynamic F2 trajectories than the poorly identified tokens. The two groups of male /æ/ vowels were classified with 94% accuracy (Wilks' = .45, p < .01). The 20 well-identified tokens on average had lower f0 values, higher F2 at steady state, longer durations, and greater change in F1 from onset to offset than the 11 poorly identified vowels. The discriminant analysis model classified the 31 good and 3 bad versions of /æ/ produced by the female speakers with 97% accuracy overall (Wilks' = .72, p < .01), although 1 of the 3 bad tokens was misclassified. The well-identified /æ/vowels had higher mean f2 values and greater change in F2 from onset to offset.
The male // vowels were classified with 97% accuracy (Wilks' = .32, p < .01). The 20 good versions were distinguished from the bad examples by having higher F1 onset values, higher F2 offset values, and lower f0. The classification rate for the 19 good and 7 bad female tokens of // was 96% (Wilks' = .42, p < .01). F1 and F2 values at onset were higher for the well-identified versions. The two groups of // vowels produced by the male talkers were classified with 93% accuracy (Wilks' = .37, p < .01). The 18 well-identified // vowels were characterized by higher f0, shorter duration, higher F2 values at onset, and less change in F2 over time than the 9 poorly identified tokens.
In summary, potentially confusable vowel pairs such as /æ/–// and //–// are distinguished from one another not only by steady-state F1 and F2 values but also by formant frequencies at vowel onsets and offsets, formant movement patterns over time, and duration differences. Good talkers produce long /æ/ vowels with relatively high F2 values and considerable change in F2 over the course of the vowel and short // vowels with relatively low F2 values and little formant movement. Relatively high F1 and F2 values appear to be important in distinguishing // from //. In addition, duration differences (relatively long for // and short for //) may also play a role in separating these spectral neighbors.
Discussion
Global measures of vowel characteristics such as mean fundamental frequency, formant frequency, or mean duration had little predictive value for vowel identification scores for the 93 men and women speakers in the Hillenbrand et al. (1995) study. Fine-grained acoustic measures characterizing vowel space or distinctiveness among vowels in the 10-vowel set were weakly correlated with vowel identification scores. Vowel space area alone predicted only 9%–12% of variance in identification scores, and the combination of vowel space area with F1 and F2 range, duration ratio between long and short vowels, and the dynamic ratio between relatively dynamic and static vowels accounted for less than one quarter of variance in identification scores.
Examination of confusion matrices revealed that the failure of talkers to adequately distinguish between the neighboring vowel pairs /æ/–// and //–// was responsible for most of the differences among vowel identification scores. Highly intelligible talkers distinguished similar vowel pairs such as /æ/–// and //–// not only by differences in F1 and F2 values at the steady state but in formant frequency differences at onset and offset, differences in duration, and different amounts of formant movement over the course of the vowel. Although formant movement and duration cues are regarded as secondary in importance to steady-state formant frequency values, their usefulness to listeners has been documented in a number of studies (e.g., Hillenbrand, Clark, & Houde, 2000; Hillenbrand & Nearey, 1999).
Regarding gender differences, the 45 male and 48 female speakers did not differ significantly in vowel identification accuracy despite significant differences in vowel production. In addition to the expected gender differences in vowel production related to vocal tract size and vocal fold mass, women produced significantly longer and more dynamic vowels in the F1 x F2 space and had larger vowel spaces than men. Previous studies have demonstrated that women produce longer vowels than men in English and other languages (e.g., Simpson & Ericsdotter, 2003) and have larger vowel areas (e.g., Simpson & Ericsdotter, 2007; Smiljanic, Viau, & Bradlow, 2006). In an attempt to explain the nonuniform differences in vowel space between men and women, Simpson and Ericsdotter (2007) suggested that talkers with higher mean f0 may increase their vowel space to compensate for poorer density of harmonic sampling. However, their analysis of German speakers did not confirm this prediction. In the Hillenbrand data set used in this study, neither vowel space area nor mean distance among vowels was correlated with mean f0 for the male and female speakers. Some gender differences in duration and vowel space area are related to anatomical phenomena, but there may also be sociophonetic differences in vowels produced by men and women. Listeners appear to incorporate these gender differences into their expectations for vowel identity.
Studies relating vowel space characteristics to vowel intelligibility and intelligibility of words and sentences have been quite variable in the strength of the correlations that are found. Differences in severity and type of population studied may account for the diverse findings. For normal speakers, vowel space area predicted only about 10% of variance in vowel identification scores in the current study. Bradlow et al. (1996) found that vowel space dispersion for /i/, //, and /o/ predicted 19% of variance in sentence intelligibility scores, and in the Hazan and Markham (2004) study, the F2 difference between /i/ and /u/ predicted 16% of variance in word intelligibility scores. Studies of dysarthric speakers with Parkinson disease and multiple sclerosis have also found relatively weak correlations between vowel space area and sentence intelligibility (McRae et al., 2002; Tjaden and Wilding, 2004). The predictive ability of vowel space area appears to increase to between 45% and 73% for disorders such as dysarthria related to ALS (Turner et al., 1995; Weismer et al., 2001) and cerebral palsy (Higgins & Hodge, 2002; Liu et al., 2005) and impaired speech due to partial glossectomy (Whitehill et al., 2006). For disorders such as ALS and removal of tongue tissue in which articulatory movements are so impaired that speakers cannot make adequate distinctions in formant frequency, duration, and formant movement to differentiate among similar vowels, vowel space area may successfully predict speech intelligibility. However, for disorders in which vowel errors are relatively uncommon and dimensions of speech other than articulation (i.e., phonatory quality, resonance balance) affect listeners' perceptions of speech, vowel space area is less likely to adequately predict intelligibility. In addition, because studies of disordered speech typically include relatively small numbers of participants, correlational analyses may be susceptible to outlier effects. Even in the current study, with more than 40 members in each gender group, removal of one or two outliers resulted in substantial changes in multiple regression values.
The results presented here provide a set of vowel space area and other vowel production characteristics for a large group of speakers of American English that can be used as normative data for studies of speech differences or disorders. Because the original experiment involved identifying vowels produced by normal talkers in quiet, the relatively small range of identification scores may have restricted the multiple regression findings. Further studies of talker differences in vowel intelligibility could employ goodness ratings in place of percent correct identification or could mitigate the ceiling effect by playing vowels in degraded conditions such as noise. In addition, acoustic characteristics of vowels from other dialects should be obtained in order to determine if the Hillenbrand et al. (1995) set represents the parameters of American English as a whole. For example, Clopper, Pisoni, and de Jong (2005) found in comparing six regional dialects that Southern talkers reduced the duration distinction between tense and lax vowels but used greater spectral change in lax vowels compared with talkers from other regions. Several databases of American English dialects now exist (e.g., Clopper et al., 2005; Hagiwara, 1997; Jacewicz, Fox, Holt, & Salmons, 2006; Thomas, 2001), so this kind of analysis can be carried out for a number of regional variants of American English.
The present results have two important implications for further research on vowel production and speech intelligibility. The first is the need to examine specific vowel errors made by listeners, attempting to relate perceptual confusions to the acoustic characteristics of the vowels involved. For disordered speech, it is expected that the type and number of perceptual confusions and the acoustic characteristics will differ with the nature and severity of the disorder. The second implication is the importance of focusing on how speakers make (or fail to make) distinctions among similar vowels using not only static F1 and F2 values but duration and formant movement characteristics as well. Future studies of vowel identification and speech intelligibility should include measures beyond static F1 and F2 frequencies in order to adequately characterize the production and perception of vowels.
Received February 1, 2007
Accepted August 29, 2007
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