High-resolution characterization of formants in vowel-consonant transitions

Two data-selective techniques that facilitate formant tracking with greater temporal resolution are presented: a time-varying autoregressive (AR) model and a short window AR model. They are used to analyze the regions of voicing immediately preceding closure in vowel-consonant transitions. Results on real speech data show that these methods detect the rapid change in the formants prior to closure better than standard linear predictive coding methods; hence, they provide important cues as to the nature of the stop. When these cues are used in conjunction with the commonly used ones from the burst, far more accurate labeling of the stops should result.<>