New cepstral representation using wavelet analysis and spectral transformation for robust speech recognition
Hubert Wassner, Gérard Chollet · 4th International Conference on Spoken Language Processing (ICSLP 1996) · 1996
The goal is to improve recognition rate by optimisation of Mel Frequency Cepstral Coecients (MFCCs): modications concern the time-frequency representations used to estimate these coecients.There are many w a ys to obtain a spectrum out of a signal which dier in the method itself (Fourier, Wavelets,...), and in the normalisation.We show here that we can obtain noise resistant cepstral coecients, for speaker independent connected word recognition.The recognition system is based on a continuous whole word hidden Markov model.An error reduction rate of approximately 50% is achieved with word models.