Title :
A family of formant trackers based on hidden Markov models
Author_Institution :
Schlumberger Palo Alto Research, Palo Alto, CA
Abstract :
This paper describes a family of formant trackers based on hidden Markov models and vector quantization of LPC spectra. Two general classes of models are presented, 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 multi-formant 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 hand-marked formant tracks. The models have been evaluated using portions of the Texas Instruments multi-dialect connected digits database. The most accurate configurations exhibited root mean square estimation errors of about 70 Hz, 95 Hz, and 140 Hz, for F1, F2and F3, respectively.
Keywords :
Databases; Estimation error; Frequency estimation; Hidden Markov models; Instruments; Linear predictive coding; Parameter estimation; Root mean square; Speech analysis; Vector quantization;
Conference_Titel :
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '86.
DOI :
10.1109/ICASSP.1986.1168811