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

Abstract

In this work the design and evaluation of the recognition performance of context dependentHidden 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 out putvectors. Initial model parameter estimates are obtained by means of an automatic segmentation procedure for careful modeling of relevant pho etic features. The model structure and the training scheme are directed to associate the most acoustically dis criminativesegments 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 transi tions.A comparative study of the recognition performance under different degrees of context dependence, and the alternative use of spectral dynamic and energy related pa rametersis presented.