Recognition of intervocalic stops in continuous speech using context-dependent HMMs

概要

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In this work the design and evaluation of the recognition performance of context-dependent Hidden Markov Models (HMMs) for the intervocalic voiced and unvoiced stops is described. The phoneme HMMs are context-dependent in order to account for coarticulatory effects. Continuous probability density functions are used for the output vectors. Initial model parameter estimates are obtained by means of an automatic segmentation procedure for careful modeling of relevant phonetic features. The model structure and the training scheme are directed to associate the most acoustically discriminative segments of the consonants with a sequence of states in every consonant model. The speech data base consisted of a total of 2, 592 productions of the Spanish Stops /p, t, k, b, d, g/ in intervocalic positions with the vowels /a, i, u/ embedded in VCVCVCV nonsense utterances. The speech data has been produced by two male Argentine Spanish speakers. Phoneme recognition is accomplished finding the state sequence with highest likelihood in an ergodic model formed by the linking of all the context-dependent phoneme models allowing only the phonotactically valid state transitions. A comparative study of the recognition performance under different degrees of context dependence, and the alternateive use of spectral dynamic and energy related parameters is presented.
社団法人日本音響学会の論文
1990-05-00