Sandoval, Steven

Purpose The use and study of formant frequencies for the description of vowels is commonplace in acoustic phonetics and in attempts to understand results of speech perception studies. Numerous studies have shown that listeners are better able to distinguish vowels when the acoustic parameters are based on spectral information extracted at multiple time points during the duration of the vowel, rather than at a single point in time. The purpose of this study was to validate an automated method for extracting formant trajectories, using information across the time course of production, and subsequently characterize the formant trajectories of vowels using a large, diverse corpus of speech samples. Method Using software tools, we automatically extract the 1st 2 formant frequencies (F1/F2) at 10 equally spaced points over a vowel's duration. Then, we compute the average trajectory for each vowel token. The 1,600 vowel observations in the Hillenbrand database and the more than 50,000 vowel observations in the TIMIT database are analyzed. Results First, we validate the automated method by comparing against the manually obtained values in the Hillenbrand database. Analyses reveal a strong correlation between the automated and manual formant estimates. Then, we use the automated method on the 630 speakers in the TIMIT database to compute average formant trajectories. We noted that phonemes that have close F1 and F2 values at the temporal midpoint often exhibit formant trajectories progressing in different directions, hence highlighting the importance of formant trajectory progression. Conclusions The results of this study support the importance of formant trajectories over single-point measurements for the successful discrimination of vowels. Furthermore, this study provides a baseline for the formant trajectories for men and women across a broad range of dialects of Standard American English.

Purpose Telemedicine, used to offset disparities in access to speech-language therapy, relies on technology that utilizes compression algorithms to transmit signals efficiently. These algorithms have been thoroughly evaluated on healthy speech; however, the effects of compression algorithms on the intelligibility of disordered speech have not been adequately explored. Method This case study assessed acoustic and perceptual effects of resampling and speech compression (i.e., transcoding) on the speech of 2 individuals with dysarthria. Forced-choice vowel identification and transcription tasks were utilized, completed by 20 naive undergraduate listeners. Results Results showed relative improvements and decrements in intelligibility, on various measures, based on the speakers' acoustic profiles. The transcoding of the speech compression algorithm resulted in an enlarged vowel space area and associated improvements in vowel identification for 1 speaker and a smaller vowel space area and decreased vowel identification for the other speaker. Interestingly, there was an overall decrease in intelligibility in the transcription task in this condition for both speakers. Conclusions There is a complex interplay between dysarthria and compression algorithms that warrants further exploration. The findings suggest that it is critical to be mindful of apparent changes in intelligibility secondary to compression algorithms necessary for practicing telemedicine. Supplemental Material https://doi.org/10.23641/asha.7291940