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Speech feature extracted from adaptive wavelet for speech recognition

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3 Author(s)
Sungwook Chang ; Dept. of Control & Instrum. Eng., Hanyang Univ., Seoul, South Korea ; Kwon, Y. ; Sang-Il Yang

The speech signal is decomposed through adapted local trigonometric transforms. The decomposed signal is classified by M uniform sub-bands for each subinterval. The energy of each sub-band is used as a speech feature. This feature is applied to vector quantisation and the hidden Markov model. The new speech feature shows a slightly better recognition rate than the cepstrum for speaker independent speech recognition. The new speech feature also shows a lower standard deviation between speakers than does the cepstrum

Published in:

Electronics Letters  (Volume:34 ,  Issue: 23 )