The use of wavelet transforms in phoneme recognition

The 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 speaker-independent HMM-based phoneme recognizer. The results are evaluated on a portion of the TIMIT corpus consisting of 30293 phoneme tokens for training and 14489 phoneme tokens for testing. The test results suggest that SCWT gives a considerably better recognition rate than DWT. On the other hand, the improvement of SCWT over Mel-scale cepstral coefficients appears to be marginal.