论文标题
单声道语音的时间卷积网络的接收场分析
Receptive Field Analysis of Temporal Convolutional Networks for Monaural Speech Dereverberation
论文作者
论文摘要
语音静脉曲张通常是强大的语音处理任务中的重要要求。有监督的深度学习（DL）模型为单渠道语音消失提供了最先进的表现。时间卷积网络（TCN）通常用于语音增强任务中的序列建模。 TCN的一个功能是，它们具有依赖于特定模型配置的接收场（RF），该模型配置确定了可以观察到的输入框架的数量来产生单个输出框架。已经表明，TCN能够对模拟语音数据进行编织，但是进行了彻底的分析，尤其是在文献中尚未关注RF。本文根据TCN的模型大小和RF分析了覆盖性能。使用WHAMR语料库进行的实验，该实验扩展到包括较大T60值的房间脉冲响应（RIR）表明，较大的RF在训练较小的TCN模型时可以显着提高性能。还证明，当用较大的RT60值放电RIR时，TCN从更宽的RF中受益。
Speech dereverberation is often an important requirement in robust speech processing tasks. Supervised deep learning (DL) models give state-of-the-art performance for single-channel speech dereverberation. Temporal convolutional networks (TCNs) are commonly used for sequence modelling in speech enhancement tasks. A feature of TCNs is that they have a receptive field (RF) dependent on the specific model configuration which determines the number of input frames that can be observed to produce an individual output frame. It has been shown that TCNs are capable of performing dereverberation of simulated speech data, however a thorough analysis, especially with focus on the RF is yet lacking in the literature. This paper analyses dereverberation performance depending on the model size and the RF of TCNs. Experiments using the WHAMR corpus which is extended to include room impulse responses (RIRs) with larger T60 values demonstrate that a larger RF can have significant improvement in performance when training smaller TCN models. It is also demonstrated that TCNs benefit from a wider RF when dereverberating RIRs with larger RT60 values.
