Abstract
The authors present a vector quantization (VQ) codebook design algorithm for recognition systems based on the phoneme-level hidden Markov model (HMM). In order to improve the discrimination ability of HMMs, the VQ codewords are extracted from the HMM state segments of the recognition units. An iterative VQ codebook design method based on the LVQ2 or MKM algorithm is proposed for the unified estimation of VQ codebooks and HMM parameters. Although the codebooks designed by the proposed algorithm showed larger quantization distortions for input speech spectrum than the conventional codebooks, they always improved the recognition accuracy of the recognition systems as compared to the conventional phoneme-independent or phoneme-dependent codebooks. >
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