Speaker adaptive phoneme recognition by multi-level clustering based on mutual information criterion

A statistical method for recognizing phonemes in continuous speech is presented. Two aspects of the system are discussed. The first aspect is speaker adaptation to improve the recognition rate. A parametric expression of speaker individuality is used, which is calculated from the spectral distortion in the vector quantization. Each acoustic feature space is divided by the speaker individuality parameter. The second is the effective calculation of phoneme likelihood, especially for consonants in various phoneme environments. Since the acoustic features of consonants are strongly dependent on the surrounding phonemes, phoneme segments which have high scores are extracted. The other parts are discriminated under the assumption that the reliable parts of phonemes really exist in the string of utterance. In the frame level, the correct recognition rate of all phoneme categories reaches 78.3% in a multispeaker experiment (six males) and 72.7% in a completely speaker-independent experiment