Auditory Contrast Spectrum for Robust Speech Recognition

Traditional speech representations are based on power spectrum which is obtained by energy integration from many frequency bands. Such representations are sensitive to noise since noise energy distributed in a wide frequency band may deteriorate speech representations. Inspired by the contrast sensitive mechanism in auditory neural processing, in this paper, we propose an auditory contrast spectrum extraction algorithm which is a relative representation of auditory temporal and frequency spectrum. In this algorithm, speech is first processed using a temporal contrast processing which enhances speech temporal modulation envelopes in each auditory filter band and suppresses steady low contrast envelopes. The temporal contrast enhanced speech is then integrated to form speech spectrum which is named as temporal contrast spectrum. The temporal contrast spectrum is then analyzed in spectral scale spaces. Since speech and noise spectral profiles are different, we apply a lateral inhibition function to choose a spectral profile subspace in which noise component is reduced more while speech component is not deteriorated. We project the temporal contrast spectrum to the optimal scale space in which cepstral feature is extracted. We apply this cepstral feature for robust speech recognition experiments on AURORA-2J corpus. The recognition results show that there is 61.12% improvement of relative performance for clean training and 27.45% improvement of relative performance for multi-condition training.