Optimized wavelet-domain filtering under noisy and reverberant conditions

Abstract

The paper addresses a robust wavelet-based speech enhancement for automatic speech recognition in reverberant and noisy conditions. We propose a novel scheme in improving the speech, late reflection, and noise power estimates from the observed contaminated signal. The improved estimates are used to calculate the Wiener gain in filtering the late reflections and additive noise. In the proposed scheme, optimization of the wavelet family and its parameters is conducted using an acoustic model (AM). In the offline mode, the optimal wavelet family is selected separately for the speech, late reflections, and background noise based on the AM likelihood. Then, the parameters of the selected wavelet family are optimized specifically for each signal subspace. As a result we can use a wavelet sensitive to the speech, late reflection, and the additive noise, which can independently and accurately estimate these signals directly from an observed contaminated signal. For speech recognition, the most suitable wavelet is identified from the pre-stored wavelets, and wavelet-domain filtering is conducted to the noisy and reverberant speech signal. Experimental evaluations using real reverberant data demonstrate the effectiveness and robustness of the proposed method.In a real-world enclosed environment, the speech signal is reflected and arrives at different time delays when observed by the microphone. This effect is considered as a form of a contamination due to channel distortion, and commonly known as reverberation. The degree of reverberation depends on the reverberation time T 60 , which dictates the severity of distortion. Speech contamination is one of the most common problems in automatic speech recognition (ASR) applications. In the perspective of ASR, any form of contamination of the speech signal at runtime (test condition) is a mismatch to the acoustic model (AM) (training condition). The mismatch may result in the degradation of the ASR performance. Thus, speech enhancement is one of the most important topics in robust ASR. In this paper, we focus primarily on the topic of dereverberation for ASR; since background noise is always present in a real environment, we address enhancement in reverberant and noisy condition, and extend our dereverberation framework to include denoising effect.The scheme of decomposition of the reverberant signal into early and late reflections [1] simplifies the treatment of reverberation. In this scheme, the late reflection

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