| COMPUTER SCIENCE AND TECHNOLOGY |
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DNN-LSTM based VAD algorithm |
| ZHANG Xueying, NIU Puhua, GAO Fan |
| College of Information Engineering, Taiyuan University of Technology, Taiyuan 030024, China |
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Abstract Voice activity detection (VAD) algorithms based on deep neural networks (DNN) ignore the temporal correlation of the acoustic features between speech frames which significantly reduces the performance in noisy environments. This paper presents a hybrid deep neural network with long-short term memory (LSTM) for VAD analyses which utilizes dynamic information from the speech frames. A context information based cost function is used to train the DNN-LSTM network. The noisy speech corpus used here was based on TIDIGITS and Noisex-92. The results show that the DNN-LSTM based VAD algorithm has better recognition accuracy than DNN-based VAD algorithms in noisy environment which shows that this cost function is more suitable than the traditional cost function.
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| Keywords
voice activity detection
deep neural network
long-short term memory
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Issue Date: 15 May 2018
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2018,
Vol. 58
