A New Concept for Feature-Domain Dereverberation for Robust Distant-Talking ASR

A New Concept for Feature-Domain Dereverberation for Robust Distant-Talking ASR
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鲁棒远距离语音 ASR 的特征域去混响新概念

DOI：
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发表时间：
2007
期刊：
IEEE International Conference on Acoustics, Speech, and Signal Processing
影响因子：
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通讯作者：
Walter Kellermann
Walter Kellermann
中科院分区：
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文献类型：
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作者：
A. Sehr;Walter Kellermann

文献摘要

被引文献

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本文研究了混响环境中自动语音识别新方法的特征域去混响能力。通过组合干净语音 HMM 网络和混响模型，在解码期间通过维特比算法的扩展版本找到 HMM 输出和混响模型输出的最可能的组合。我们在本文中表明，最有可能的 HMM 输出代表了对干净语音特征序列的良好估计，并且可以用作后续语音识别器的输入。
The feature-domain dereverberation capabilities of a novel approach for automatic speech recognition in reverberant environments are investigated in this paper. By combining a network of clean speech HMMs and a reverberation model, the most likely combination of the HMM output and the reverberation model output is found during decoding time by an extended version of the Viterbi algorithm. We show in this paper that the most likely HMM output represents a good estimate of the clean speech feature sequence and can be used as input to subsequent speech recognizers.