Nonintrusive Speech Intelligibility Prediction Using Convolutional Neural Networks
Nonintrusive Speech Intelligibility Prediction Using Convolutional Neural Networks
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DOI：
10.1109/taslp.2018.2847459
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发表时间：
2018-10-01
影响因子：
5.4
通讯作者：
Jensen, Jesper
中科院分区：
文献类型：
--
作者：
Andersen, Asger Heidemann;de Haan, Jan Mark;Jensen, Jesper
语音可懂度预测（SIP）算法正成为语音处理设备和算法的开发和操作中的流行工具。然而，许多SIP算法需要了解底层的干净语音;在现实世界的应用中通常无法获得的信号。这导致人们对非侵入式SIP算法的兴趣增加，这种算法不需要干净的语音来进行预测。在本文中，我们研究了使用卷积神经网络（CNN）的非侵入式SIP。为此，我们利用CNN架构，该架构在计算结构方面与现有SIP算法具有相似性，并且允许对训练权重进行简单而有意义的可视化和解释。我们评估这种架构使用一个大的数据集，通过结合文献中的数据集。该方法显示出较高的预测性能相比，现有的四个侵入式和非侵入式SIP算法。这证明了深度学习在语音清晰度预测方面的潜力。
Speech Intelligibility Prediction (SIP) algorithms are becoming popular tools within the development and operation of speech processing devices and algorithms. However, many SIP algorithms require knowledge of the underlying clean speech; a signal that is often not available in real-world applications. This has led to increased interest in nonintrusive SIP algorithms, which do not require clean speech to make predictions. In this paper, we investigate the use of Convolutional Neural Networks (CNNs) for nonintrusive SIP. To do so, we utilize a CNN architecture that shows similarities to existing SIP algorithms, in terms of computational structure, and which allows for easy and meaningful visualization and interpretation of trained weights. We evaluate this architecture using a large dataset obtained by combining datasets from the literature. The proposed method shows high prediction performance when compared with four existing intrusive and nonintrusive SIP algorithms. This demonstrates the potential of deep learning for speech intelligibility prediction.