A Bayesian framework for robust speech enhancement under varying contexts

D. Hanumantha, Rao Naidu, Sriram Srinivasan
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引用次数: 6

Abstract

Single-microphone speech enhancement algorithms that employ trained codebooks of parametric representations of speech spectra have been shown to be successful in the suppression of non-stationary noise, e.g., in mobile phones. In this paper, we introduce the concept of a context-dependent codebook, and look at two aspects of context: dependency on the particular speaker using the mobile device, and on the acoustic condition during usage (e.g., hands-free mode in a reverberant room). Such context-dependent codebooks may be trained on-line. A new scheme is proposed to appropriately combine the estimates resulting from the context-dependent and context-independent codebooks under a Bayesian framework. Experimental results establish that the proposed approach performs better than the context-independent codebook in the case of a context match and better than the context-dependent codebook in the case of a context mismatch.
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不同语境下稳健语音增强的贝叶斯框架
使用经过训练的语音频谱参数表示码本的单麦克风语音增强算法已被证明在抑制非平稳噪声方面是成功的,例如在移动电话中。在本文中,我们介绍了上下文相关码本的概念,并从两个方面考察了上下文:对使用移动设备的特定扬声器的依赖,以及使用过程中的声学条件(例如,混响室中的免提模式)。这种与上下文相关的密码本可以在线培训。在贝叶斯框架下,提出了一种将上下文相关和上下文无关的码本估计相结合的新方案。实验结果表明,该方法在上下文匹配的情况下优于上下文无关的码本,在上下文不匹配的情况下优于上下文依赖的码本。
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