This study investigates the usefulness of wavelet transforms in phoneme recognition. Both discrete wavelet transforms (DWT) and sampled continuous wavelet transforms (SCWT) are tested. The wavelet transform is used as a part of the front-end processor which extracts feature vectors for a speakerindependent HMM-based phoneme recognizer. The results are evaluated on a portion of TIMIT corpus consisting of 30293 phoneme tokens for training and 14489 phoneme tokens for testing. The test results suggest that SCWT gives considerably better recognition rate than DWT. On the other hand, the improvement of SCWT over Mel-scale cepstral coecients appears to be marginal.

We propose the design of a hearing aid device based on the wavelet transform. The fast wavelet transform is used to decompose speech into different frequency components. This paper presents the difficulties in the use of wavelet transforms for speech processing, and shows how the careful selection of wavelet coefficients can enable the four major categories of speech -voiced speech, plosives, fricatives and silence to be identified. With knowledge of these four categories, it is shown how speech can be easily and effectively segmented.

This study investigates the usefulness of wavelet transforms in phoneme recognition. Both discrete wavelet transforms (DWT) and sampled continuous wavelet transforms (SCWT) are tested. The wavelet transform is used as a part of the front-end processor which extracts feature vectors for a speakerindependent HMM-based phoneme recognizer. The results are evaluated on a portion of TIMIT corpus consisting of 30293 phoneme tokens for training and 14489 phoneme tokens for testing. The test results suggest that SCWT gives considerably better recognition rate than DWT. On the other hand, the improvement of SCWT over Mel-scale cepstral coecients appears to be marginal.

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