An endpoint detection algorithm based on MFCC and spectral entropy using BP NN

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 they 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.