An Endpoint Detection Algorithm Based on MFCC And Spectral Entropy Using BP NN

摘要：

Endpoint detection is the preliminary job of speech signal processing, it is vital to speech recognition Most of recent endpoint detection algorithms will give a satisfied result at high SNRs (signal-to-noise ratio), while tbey might fail in occasion where the noise level is too excessive. In this paper, a novel endpoint detection algorithm based on 12-order MFCC and spectral entropy in the framework of BP NN is presented. It can be shown by the experiments that the proposed method is more reliable and efficient than the traditional ones based on short-term energy at low SNRs.

关键词： endpoint detection BP neural network MFCC spectral entropy short-term energy

作者: Haiying Zhang Hailong Hu

作者单位: Software school, Xiamen University Xiamen, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 1349-1353

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）