基于临界频带及能量熵的语音端点检测

Speech endpoint detection based on critical band and energy entropy

语音端点检测的准确性直接关系着语音识别、合成、增强等语音领域的准确性,为了提高语音端点检测的有效性,提出了一种基于临界频带及能量熵的语音端点检测算法。算法充分利用人耳听觉特性的频率分布,将含噪语音信号进行临界频带划分,并结合各频带内信号的能量熵值在语音段和噪声段的不同分布,实现不同背景噪声下语音端点检测。实验结果表明,提出的语音端点检测算法与传统的短时能量法相比,检测正确率平均高1.6个百分点。所提方法在不同噪声的低信噪比(SNR)环境下均能实现语音端点检测。

The accuracy of the speech endpoint detection has a direct impact on the precision of speech recognition, synthesis, enhancement, etc. To improve the effectiveness of speech endpoint detection, an algorithm based on critical band and energy entropy was proposed, h took full advantage of the frequency distribution of human auditory characteristics, and divided the speech signals according to critical bands. Combined with the different distribution of energy entropy of each critical band of the signals respectively in the speech segments and noise segments, speech endpoint detection under different background noises was completed. The experimental results indicate that the average accuracy of the newly proposed algorithm is 1.6% higher than the traditional short-time energy algorithm. The proposed method can achieve the detection of speech endpoint under various noise environment of low Signal to Noise Ratio （SNR）.