A new normalisation technique for speech recognition in adverse conditions is presented. Specifically the influence of additive noise in combination with convolutive distortions is considered. In the proposed method a masking constant is added to the outputs of a mel scale triangular filterbank. This is done for testing and training samples. The goal is to normalize the signal-to-noise ratio (SNR) in each frequency band by adapting the masking constant depending on the measured SNR or dynamic range in each band. This makes the extracted parameters less sensitive to the noise level, but also the influence of channel distortions is suppressed. The method is easy to implement and works on-line. Experimental results are given on the NOISEX-92 database and on real car data.
['Tom Claes', 'D. Van Compernolle']
SNR-normalisation for robust speech recognition