基于自适应倒谱距离的强噪声语音端点检测

Adaptive Cepstral Distance-based Voice Endpoint Detection of Strong Noise

在有噪声干扰的情况下,传统的语音端点检测方法的检测准确度明显下降。为了在强背景噪声环境下有效区分出语音信号和非语音信号,针对倒谱距离端点检测方法进行了研究,提出了一种基于自适应倒谱距离的强噪声语音端点检测方法。本方法引入倒谱距离乘数和门限增量系数,针对不同信噪比采用不同的倒谱距离乘数,并采用自适应判决门限的方法进行语音端点检测。MATLAB仿真实验结果显示,在不同背景噪声和不同信噪比下,本方法对于语音端点检测具有较高的检测正确率,其端点检测效果明显优于传统端点检测方法,适用于强背景噪声下的端点检测。

In the case of noise interference, accuracy of speech endpoint detection using the traditional method dramati- cally declines. In order to effectively distinguish the speech signal and non-voice signal in strong background noise envi- ronment, this paper presented a strong noise speech endpoint detection method based on adaptive cepstral distance. The method introduces cepstral distance multiplier and the threshold increment coefficient. Different cepstral distance multi- pliers are used for different SNR and adaptive decision threshold method is used for voice activity detection. MATLAB simulation results show that, under different background noise and different SNR, the method for voice activity detection has high detection accuracy. Its detection is better than the traditional endpoint detection method, and is suitable for endpoint detection under strong background noise.