DNN-LSTM based VAD algorithm

ZHANG Xueying; NIU Puhua; GAO Fan

doi:10.16511/j.cnki.qhdxxb.2018.25.022

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Journal of Tsinghua University(Science and Technology) ›› 2018, Vol. 58 ›› Issue (5) : 509-515. DOI: 10.16511/j.cnki.qhdxxb.2018.25.022

COMPUTER SCIENCE AND TECHNOLOGY

DNN-LSTM based VAD algorithm

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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.

Key words

voice activity detection / deep neural network / long-short term memory

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ZHANG Xueying, NIU Puhua, GAO Fan. DNN-LSTM based VAD algorithm[J]. Journal of Tsinghua University(Science and Technology). 2018, 58(5): 509-515 https://doi.org/10.16511/j.cnki.qhdxxb.2018.25.022

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