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Formant tracking using hidden Markov models and vector quantization

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1 Author(s)
Kopec, G. ; Schlumberger Palo Alto Research, Palo Alto, CA

This paper describes an approach to formant tracking based on hidden Markov models and vector quantization of LPC spectra. Two general classes of models are developed, differing in whether formants are tracked singly or jointly. The states of a single-formant model are scalar values corresponding to possible formant frequencies. The states of a multiformant model are frequency vectors defining possible formant configurations. Formant detection and estimation are performed simultaneously using the forward-backward algorithm. Model parameters are estimated from handmarked formant tracks. The models have been evaluated using portions of the Texas Instruments multidialect connected digits database. The most accurate configurations exhibited root-mean-square estimation errors of about 70, 95, and 140 HZ, for F1, F2, and F3, respectively.

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Acoustics, Speech and Signal Processing, IEEE Transactions on  (Volume:34 ,  Issue: 4 )