低信噪比下采用感知语谱结构边界参数的语音端点检测算法

Speech endpoint detection in low-SNRs environment based on perception spectrogram structure boundary parameter

提出了一种采用感知语谱结构边界参数(PSSB)的语音端点检测算法,用于在低信噪比环境下的语音信号预处理。在对含噪语音进行基于听觉感知特性的语音增强之后,针对语音信号的连续分布特性与残留噪声的随机分布特性之间的不同点,对增强后语音的时-频语谱进行二维增强,从而进一步突出连续分布的纯净语音的语谱结构。通过对增强后语音语谱结构的二维边界检测,提出PSSB参数,并用于端点检测。实验结果表明,在白噪声-10 dB到10 dB的各种信噪比环境下,采用PSSB参数的端点检测算法,相对于其它端点检测算法,更有效地检测出语音的端点。在-10 dB的极低信噪比下,提出的方法仍然有75.2%的正确率。采用PSSB参数的端点检测算法,更适合于低信噪比白噪声环境下的语音端点检测。

A Perception Spectrogram Structure Boundary （PSSB） parameter is proposed for speech endpoint detection as a preprocess of speech signal. A hearing perception speech enhancement is made as a first step, then a two-dimensional enhancement is performed upon the speech spectrogram according to the difference between the continuous distribution characteristic of pure speech and the random distribution characteristic of noise, in order to emphasize the continuous spectrogram structure of pure speech. PSSB parameter is proposed based on the two-dimensional boundary detection of the enhanced speech spectrogram structure. Experimental results show that, in a variety of SNR environments from -10 dB to 10 dB, the algorithm proposed in this paper can achieve higher accuracy in comparison to the extant endpoint detection algorithms. With our algorithm, accuracy of 75.2% can be reached even in the extreme low SNR at -10 dB. The endpoint detection algorithm using PSSB, is suitable for speech endpoint detection in low-SNRs environment with white noise.