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논문 기본 정보

자료유형
학술저널
저자정보
An, Soo Jeong (Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology) Choi, Seung Ho (Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology)
저널정보
한국인터넷방송통신학회 International journal of internet, broadcasting and communication : IJIBC International journal of internet, broadcasting and communication : IJIBC 제11권 제4호
발행연도
2019.1
수록면
26 - 30 (5page)

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This paper proposes a new voice activity detection (VAD) method which is based on SNR and non-intrusive speech intelligibility estimation. In the conventional SNR-based VAD methods, voice activity probability is obtained by estimating frame-wise SNR at each spectral component. However these methods lack performance in various noisy environments. We devise a hybrid VAD method that uses non-intrusive speech intelligibility estimation as well as SNR estimation, where the speech intelligibility score is estimated based on deep neural network. In order to train model parameters of deep neural network, we use MFCC vector and the intrusive speech intelligibility score, STOI (Short-Time Objective Intelligent Measure), as input and output, respectively. We developed speech presence measure to classify each noisy frame as voice or non-voice by calculating the weighted average of the estimated STOI value and the conventional SNR-based VAD value at each frame. Experimental results show that the proposed method has better performance than the conventional VAD method in various noisy environments, especially when the SNR is very low.

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