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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Joint sparse representation based cepstral-domain dereverberation for distant-talking speech recognition
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Joint sparse representation based cepstral-domain dereverberation for distant-talking speech recognition

机译：基于联合稀疏表示的倒谱域去混响用于远距离语音识别

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摘要

In this paper we address reducing the mismatch between training and testing conditions for robust distant-talking speech recognition under realistic reverberant environments. It is well known that the distortions caused by reverberation, background noise, etc., are highly nonlinear in the cepstral domain. In this paper we propose to capture the complex relationships between clean and reverberant speech via joint dictionary learning. Given a test reverberant speech with a sequence of feature vectors we first find their sparse representations, and then estimate the underlying clean feature vectors using the dictionary of clean speech. Based on speech recognition experiments conducted under realistic reverberation conditions, the proposed method is shown to perform very well, resulting in an average relative improvement of 59.1% compared with the baseline front-ends.
机译：在本文中，我们解决了在真实混响环境下减少鲁棒的远距离语音识别的训练条件与测试条件之间的不匹配问题。众所周知，由混响，背景噪声等引起的失真在倒频谱域中是高度非线性的。在本文中，我们建议通过联合词典学习来捕获干净语音和混响语音之间的复杂关系。给定具有一系列特征向量的测试混响语音，我们首先找到它们的稀疏表示，然后使用纯净语音字典估计基本的纯净特征矢量。基于在真实混响条件下进行的语音识别实验，所提出的方法表现出很好的效果，与基准前端相比平均可提高59.1％。

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