Earticle

초록

영어

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.

목차

Abstract
1. INTRODUCTION
2. VAD BASED ON SNR ESTIMATION
3. NON-INTRUSIVE SPEECH INTELLIGIBILITY ESTIMATION
4. PROPOSED HYBRID VAD METHOD
5. EXPERIMENTS AND RESULTS
4. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Soo Jeong An [ Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, Korea ]
• Seung Ho Choi [ Dept. of Electronic and IT Media Engineering, Seoul National University of Science and Technology, Seoul, Korea ] Corresponding author

참고문헌

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간행물

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• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380