Since wavelet decomposition of signals provides more flexible time-frequency resolutions, it can be utilized as a feature set for speech recognition. The authors explore the possibility of using wavelet decomposition for speech recognition. In particular, they investigate a modified octave structured 5-level filter bank and the HMM (hidden Markov model) is used as a recognizer. We present an analysis of various wavelet filters for speech recognition and compare the results with the conventional features that include LPC and mel-cepstrums.
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Kim et al. (2002) studied this question.
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