Channel normalization techniques for automatic speech recognition over the telephone
In this paper we aim to identify the underlying causes that can explain the performance of different channel normalization techniques. To this aim we compared four different channel normalization techniques within the context of connected digit recognition over telephone lines: cepstrum mean subtraction, the dynamic cepstrum representation, RASTA filtering and phase-corrected RASTA. We used context-dependent and context-independent hidden Markov models that were trained using a wide range of different model complexities. The results of our recognition experiments indicate that each channel normalization technique should preserve the modulation frequencies in the range between 2 and 16 Hz in the spectrum of the speech signals. At the same time, DC components in the modulation spectrum should be effectively removed. With context-independent models the channel normalization filter should have a flat phase response. Finally, for our connected digit recognition task it appeared that cepstrum mean subtraction and phase-corrected RASTA performed equally well for context-dependent and context-independent models when equal amounts of model parameters were used.
automatic speech recognition phase-corrected rasta telephone speech channel normalisation