基于深度神经网络的无监督域自适应语音活动检测

Xiao-Lei Zhang
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引用次数: 9

摘要

训练语料库与测试语料库之间的不匹配问题阻碍了基于机器学习的语音活动检测(VAD)的实际应用。在本文中,我们试图通过无监督域自适应技术来解决这个问题,该技术试图在不匹配的语料库之间找到一个共享的特征子空间。采用去噪深度神经网络作为学习机。采用三种域自适应技术进行分析。实验结果表明,无监督域自适应技术很有希望解决VAD的不匹配问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Unsupervised domain adaptation for deep neural network based voice activity detection
The mismatching problem between the training and test speech corpora hinders the practical use of the machine-learning-based voice activity detection (VAD). In this paper, we try to address this problem by the unsupervised domain adaptation techniques, which try to find a shared feature subspace between the mismatching corpora. The denoising deep neural network is used as the learning machine. Three domain adaptation techniques are used for analysis. Experimental results show that the unsupervised domain adaptation technique is promising to the mismatching problem of VAD.
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