智能语音识别系统中噪声估计算法的研究和改进

doi:10.12052/gdutxb.170173

Abstract

Abstract: The research of intelligent speech recognition technology has been going on for a long time. However, due to the characteristics of variability, instantness, continuity and dynamic of the speech signal itself, the identification of the speech still has some difficulties when the machine is put in different environments, especially in the noisy environment. In order to improve the recognition accuracy of the noisy speech signal, a commonly used noise estimation algorithm was studied, which was based on the time-averaged algorithm of posterior signal noise ratio. And an improved algorithm of the smoothing factor was brought up on the basis of the previous algorithm. The voice activity detection algorithm and the above two algorithms were simulated under different input signal-noise ratios. The comparative analysis of the operation results shows that the improved algorithm can improve the output segment SNR by 2.1 dB compared with the voice activity detection algorithm, and it can also improve the output segment SNR by 0.5 dB compared with the original time recursive average algorithm. It is indicated that the improved algorithm can effectively improve the quality and intelligibility of the speech signal at low input SNR.

Key words: speech recognition, noise estimation, time averaged algorithm, smoothing factor

CLC Number:

TP391.42

Wu Nan, Feng Zu-yong, Wei Gao-wu. Research and Improvement of Noise Estimation Algorithm in Intelligent Speech Recognition System[J].Journal of Guangdong University of Technology, 2018, 35(03): 43-46.

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