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摘要
En 中文
In this paper, a new Wiener filtering-based method for speech enhancement is described. Standard Wiener filtering formulation requires an iterative estimation of the clean speech spectrum. However, the proposed algorithm is non-iterative, therefore the computation is much faster. In addition, it employs a time-varying noise suppression factor which is based on the frame-by-frame SNR. This feature gives us the ability to suppress those parts of the degraded signal, where speech is not likely to be present and not to Suppress, and hence not to distort the speech segments as much. The algorithm is tested under simulated and actual car noise conditions and is shown to pet-form substantially better than the well-known spectral Subtraction method in both subjective and objective speech quality evaluations. Proposed method also outperformed well-known minimum mean-square error (MMSE) short-time spectral amplitude estimator technique in terms of subjective quality. In addition, the proposed method is shown to improve the robustness of a speech recognition system significantly. (C) 2005 Elsevier B.V. All rights reserved.
Keyword:
speech enhancement
noise suppression
time-varying SNR
Wiener filter
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