Compensation of channel and noise distortions combining normalization and speech enhancement techniques

Abstract

This paper introduces two techniques to obtain robust speech recognition devices in mismatch conditions (additive noise mismatch and channel mismatch). The first algorithm, adaptive Gaussian attenuation algorithm (AGA), is a speech enhancement technique developed to reduce the effects of additive background noise in a wide range of signal noise ratio (SNR) and noise conditions. The technique is closely related to the classical noise spectral subtraction (SS) scheme, but in the proposed model the mean and variance of noise are used to better attenuate the noise. Information of the SNR is also introduced to provide adaptability at different SNR conditions. The second algorithm, cepstral mean normalization and variance-scaling technique (CMNVS), is an extension of the cepstral mean normalization (CMN) technique to provide robust features to convolutive and additive noise distortions. The requirements of the techniques are also analyzed in the paper. Combining both techniques the relative channel distortion effects were reduced to 90% on the HTIMIT task and the relative additive noise effects were reduced to 77% using the TIMIT database mixed with car noises at different SNR conditions.