计算机工程2019，Vol.45Issue(2)：250-257,8.DOI:10.19678/j.issn.1000-3428.0049473
基于多特征融合与动态阈值的语音端点检测方法
Speech Endpoint Detection Method Based on Multi-Feature Fusion and Dynamic Threshold
摘要
Abstract
In view of low signal-to-noise ratio and non-stationary noise environment, the traditional methods based on feature detection have the low accuracies and poor stabilities. To solve this problem, this paper proposes a new speech endpoint detection method. The spectral subtraction method is used to reduce noise. Then the MFCC cepstrum distance features of the speech signal after spectral subtraction and the leading silent frame are extracted, and also the frequency variance characteristics of the uniform sub-band are extracted. And the dynamic threshold updating mechanism is used to detect the noisy speech with two-parameter double threshold method. Experimental results show that, compared with the method based on DWT-MFCC cepstrum distance and the method based on spectral subtraction and uniform sub-band variance, the proposed method has a higher accuracy and the lower miss rate and error rate.关键词
端点检测/谱减/MFCC倒谱距离/均匀子带方差/动态阈值更新Key words
endpoint detection/spectral subtraction/MFCC cepstrum distance/uniform sub-band variance/dynamic threshold update分类
信息技术与安全科学引用本文复制引用
朱春利,李昕..基于多特征融合与动态阈值的语音端点检测方法[J].计算机工程,2019,45(2):250-257,8.基金项目
上海市科委重点项目 (14DZ1206302). （14DZ1206302）
