New cepstral representation using wavelet analysis and spectral transformation for robust speech recognition

The goal is to improve the speech recognition rate by optimisation of mel frequency cepstral coefficients (MFCCs): modifications concern the time-frequency representations used to estimate these coefficients. There are many ways to obtain a spectrum out of a signal which differ in the method itself (Fourier, wavelets,...), and in the normalisation. We show that we can obtain noise resistant cepstral coefficients, 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.