Stress correlates and vowel targets in Tongan

In this study, we determine the acoustic correlates of primary and secondary stress in Tongan. Vowels with primary stress show differences in F0, intensity, duration, F1, and voice quality, but F0 is the best predictor of primary stress. Vowels with secondary stress are mainly cued by a difference in F0. With regards to the effects of stress on the vowel space, we find that all five Tongan vowels are higher in the vowel space (have lower F1) when unstressed, with no differences in F2. Moreover, there is no reduction in the overall size of the vowel space. We interpret this pattern as evidence that unstressed vowels in Tongan are not undergoing centralization, nor are they otherwise reduced. Rather, Tongan speakers have separate targets for stressed and unstressed vowels

Phonetics of Tongan stress

In this study, we determine the acoustic correlates of primary and secondary stress in Tongan. Vowels with primary stress show differences in F0, intensity, duration, F1, and spectral measures compared to unstressed vowels, but a linear discriminant analysis suggests F0 and duration are the best cues for discriminating vowels with primary stress from unstressed vowels. Vowels with secondary stress are mainly marked by differences in F0 relative to unstressed vowels. With regards to the effects of stress on the vowel space, we find that all five Tongan vowels are higher in the vowel space (have lower F1) when unstressed. Moreover, there is no reduction in the overall size of the vowel space. We interpret this pattern as evidence that unstressed vowels in Tongan are not prone to centralization, vowel reduction, or undershoot. The results, however, are consistent with a sonority expansion account (Beckman et al. 1992), whereby stressed vowels are lowered to enhance sonority

Online perception of glottalized coda stops in American English

In American English, voiceless codas /t/ and /p/ are often glottalized: They have glottal constriction that results in creaky voice on the preceding vowel. Previous claims suggest that such glottalization can serve to enhance /t/ or, more generally, voicelessness of coda stops. In this study, we examine the timecourse of word recognition to test whether glottalization facilitates the perception of words ending in voiceless /t/ and /p/, which is expected if glottalization is in fact enhancing. Sixty American English listeners participated in an eye-tracking study, where they heard resynthesized glottalized and non-glottalized versions of CVC English words ending in /p, t, b, d/ while looking at a display with two words presented orthographically. Target words were presented with a minimal pair differing in place of articulation (e.g., cop-cot), or voicing, (e.g., bat-bad, cap-cab). Although there is little evidence that glottalization facilitates recognition of words ending in /t/ or /p/, there is a strong inhibitory effect: Words ending in voiced stops are recognized more slowly and poorly when the preceding vowel was glottalized. These findings lend little support to a listener-driven, enhancement-based explanation for the occurrence of coda glottalization in American English. On the other hand, they suggest that glottalized instances of coda /t/ and /p/, but not of coda /d/ and /b/, are perceived as equally good variants of these sounds

Relative importance of phonation cues in White Hmong tone perception

BLS 38: General Session and Thematic Session on Language Contac

WPP, No.110: Perception of spectral slopes and tone identification in White Hmong

This study investigates the importance of source spectrum slopes in the perception of phonation by White Hmong listeners. In White Hmong, non-modal phonation (breathy or creaky voice) accompanies certain lexical tones, but its importance in tonal contrasts is unclear. In this study, native listeners participated in two perception tasks, in which they were asked to identify the word they heard. In the _rst task, participants heard natural stimuli with manipulated F0 and duration (phonation unchanged). Results indicate that phonation is important in identifying the breathy tone, but not the creaky tone. Thus, breathiness can be viewed as contrastive in White Hmong. Next, to understand which parts of the source spectrum listeners use to perceive contrastive breathy phonation, source spectrum slopes were manipulated in the second task to create stimuli ranging from modal to breathy sounding, with F0 held constant. Results indicate that changes in H1-H2 (di_erence in amplitude between the _rst and second harmonics) and H2-H4 (di_erence in amplitude between the second and fourth harmonics) are independently important for distinguishing breathy from modal phonation, consistent with the view that the percept of breathiness is inuenced by a steep drop in harmonic energy in the lower frequencies

Perceptual importance of time-domain features of the voice source.

Our previous study examined the perceptual adequacy of different source models. We found that perceived similarity between modeled and natural voice samples was best predicted (in the time dimension) by thematch between waveforms at the negative peak of the flow derivative (R(2) = 0.34). The extent of fit during the opening phase of the source pulses added only 2% to perceived match. However, in that study model, fitting was unweighted, and results might differ if another approach were used. In this study, we constrained the models to fit the negative peak of the flow derivative precisely. We fit 6 different source models to 40 natural voice sources, and then generated synthetic copies of the voices using each modeled source pulse, with all other synthesizer parameters held constant. We then conducted a visual sort-and-rate task in which listeners assessed the extent of perceived match between the original natural voice samples and each copy. Discussion will focus on the specific strengths and weaknesses of each modeling approach for characterizing differences in vocal quality, and on the importance of matches to specific time-domain events versus spectral features in determining voice quality. [Work supported by NIH/NIDCD grant DC01797 and NSF grant IIS-1018863.]

Perceptual importance of time-domain features of the voice source.

Our previous study examined the perceptual adequacy of different source models. We found that perceived similarity between modeled and natural voice samples was best predicted (in the time dimension) by thematch between waveforms at the negative peak of the flow derivative (R(2) = 0.34). The extent of fit during the opening phase of the source pulses added only 2% to perceived match. However, in that study model, fitting was unweighted, and results might differ if another approach were used. In this study, we constrained the models to fit the negative peak of the flow derivative precisely. We fit 6 different source models to 40 natural voice sources, and then generated synthetic copies of the voices using each modeled source pulse, with all other synthesizer parameters held constant. We then conducted a visual sort-and-rate task in which listeners assessed the extent of perceived match between the original natural voice samples and each copy. Discussion will focus on the specific strengths and weaknesses of each modeling approach for characterizing differences in vocal quality, and on the importance of matches to specific time-domain events versus spectral features in determining voice quality. [Work supported by NIH/NIDCD grant DC01797 and NSF grant IIS-1018863.]

WPP, No. 108: Phonation Contrasts Across Languages

This study compared the contrastive phonation types of four languages—Gujarati (modal vs. breathy), Hmong (modal vs. breathy vs. creaky), Mazatec (modal vs. breathy vs. creaky), and Yi (tense vs. lax)—on several acoustic measures, within and across languages. For Gujarati, Hmong, and Yi, two electroglottographic (EGG) measures were also compared; a Contact Quotient measure distinguished the within-language phonation types in all three languages. While several acoustic measures distinguished phonation types within each language, only H1*-H2* did so in all four languages. However, when each within-language phonation category was then compared across languages, each category was found to differ from language to language on multiple acoustic measures, e.g. breathy in Hmong is distinct from breathy in Gujarati. This unexpected result suggests that language/speaker differences in voice quality are larger than phonation category differences. This suggestion finds support in a Multi-Dimensional Scaling analysis of the acoustic measures. A three-dimensional space turns out to mostly distinguish the languages, less so the phonations. The phonation categories do not form clusters in this space across languages as might have been expected, but they do occupy separate regions along the third dimension of the space, a dimension correlated especially with H1*-H2*. Thus H1*-H2* is again seen to be the most important measure of phonation contrasts across languages