Speech classification for enhancing single channel blind dereverberation
Speech classification for enhancing single channel blind dereverberation
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DOI：
10.5281/zenodo.41256
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发表时间：
2008-08
期刊：
影响因子：
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通讯作者：
S. Fortune;J. Hopgood
中科院分区：
文献类型：
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作者：
S. Fortune;J. Hopgood
存在几种单通道去混响技术，其增强有声语音的谐波特性，或者利用无声语音的信号模型。本文演示了如何现有的语音去混响方法可以通过将语音分类为有声，无声和无声段进行改进。与使用整个信号相比，增强浊音语音的谐波特征的方法可以受益于仅增强浊音段。从输入中移除无声帧可以额外地有益于去混响方法。额外的功能，可用于去混响，信号熵和最小化的能量的静默期，介绍并表现出良好的性能。然而，语音分类是更困难的混响语音比干净的语音。一些不同的分类措施的性能进行了比较，在混响环境。它示出了性能如何随着混响的增加而降低，但是一些分类器确实比其他分类器更好地保持其性能。使用各种信号特征的去混响滤波器参数的估计的准确性进行了比较。此外，几个信号特征可以组合成一个成本函数。这显示了通过查看和增强更丰富的语音特征集来提供改进的总体估计精度的希望。
Several single channel dereverberation techniques exists that enhance the harmonic properties of voiced speech, or utilise a signal model of unvoiced speech. This paper demonstrates how existing speech dereverberation methods can be improved by classifying speech into voiced, unvoiced and silent segments. Methods that enhance the harmonic features of voiced speech can benefit from enhancing only voiced segments, compared to using the entire signal. Removing silent frames from the input can additionally benefit dereverberation methods. Additional features that can be used for dereverberation, signal entropy and minimising the energy of silent periods, are introduced and show good performance. However, speech classification is more difficult for reverberant speech than clean speech. The performance of a number of different classification measures are compared in a reverberant environment. It is shown how performance degrades with increasing reverberation, but some classifiers do hold their performance better than others. The accuracy of the estimation of a dereverberation filter parameter using various signal features are compared. In addition, several signal features can be combined into one cost function. This shows promise in giving improved overall estimation accuracy, by looking at and enhancing a richer set of speech features.