关键词: 语音端点检测, 长时信号频谱变化, 低信噪比, 非平稳噪声

Abstract: Voice activity detection is the basic work in speech signal processing. In order to improve the accuracy of voice activity detection in low signal-to-noise ratio (SNR) and nonstationary noise, a speech feature based on long-term power spectrum variability (LPSV) is proposed, and the application prospect of this feature in voice activity detection is tested by the threshold decision method. Firstly, the long-term power spectrum variability of the input signal is calculated. Then, a judgment is made with the initial threshold, and the threshold is updated adaptively according to the judgment result. Finally, whether current target frame is voice or not depends on the result of a voting mechanism. The simulation results show that compared with two classical algorithms using long-term feature (long-term signal variability, LTSV and long-term spectral flatness measure, LSFM), the proposed method can achieve higher accuracy in different noise and SNR conditions. Especially in the non-stationary noise environment, the accuracy of voice activity detection is significantly improved: in machine gun noise condition, the average accuracy increases more than 10%.

Key words: voice activity detection, long-term power spectrum variability (LPSV), low signal-to-noise ratio, nonstationary noise