Phoneme recognition in connected speech using both static and dynamic properties of spectrum described by vector quantization

This paper describes an approach for a phoneme recognition based on a clustering method which considers phonemic features in each frame. In the clustering, both acoustic and phonemic features of speech are used. The acoustic features are LPC cepstral coefficients, the cepstral changes between adjacent frames and the power changes. The combination of these features shows both the static and dynamic properties of the spectrum. The phonemic feature in a frame is composed of a triplet of phonemic symbols. A vector quantization method is applied for the clustering. Experiment of extracting phonemic label sequences is performed, considering a contiguity of code sequences between the input and the reference phonemic patterns.