Voice activity detection based on deep neural networks and Viterbi

2017 2nd International Seminar on Advances in Materials Science and Engineering

Bai, Liang^1 ; Zhang, Zhen^1 ; Hu, Jun^1

National Computer Network Emergency Response Technical Team, Coordination Center of China, Beijing

100029, China^1

关键词: Its efficiencies;    Noisy environment;    Real time performance;    Viterbi;    Voice activity detection;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/231/1/012042/pdf
DOI  :  10.1088/1757-899X/231/1/012042

来源: IOP

【 摘 要 】

Voice Activity Detection (VAD) is important in speech processing. In the applications, the systems usually need to separate speech/non-speech parts, so that only the speech part can be dealt with. How to improve the performances of VAD in different noisy environments is an important issue in speech processing. Deep Neural network, which proves its efficiency in speech recognition, has been widely used in recent years. This paper studies the present typical VAD algorithms, and presents a new VAD algorithm based on deep neural networks and Viterbi algorithm. The result demonstrates the effectiveness of the deep neural network with Viterbi used in VAD. In addition, it shows the flexibility and the real-time performance of the algorithms.