Research on speech endpoint detection under low signal-to-noise ratios
Han Zhiyan, Jian Wang · 2015
A novel speech endpoint detection algorithm was proposed to improve the accuracy in low signal-to-noise ratio (SNR) conditions. Core technology was based on the complementarity between the short-time energy-zero-product and discrimination information, which used short-time energy-zero-product algorithm to make judgment firstly, and then used discrimination information based on the sub-band energy distribution probabilities algorithm to recheck when met with the transition for noise frame and speech frame, so as to avoided error-detected owing to the sharp change of noise amplitude and the ending speech frames which were polluted by noise. Moreover, we proposed a novel dynamically update the noise energy threshold algorithm, which could trace the changes for noise energy better. The simulation experimental results show that the new method gives a precise and rapid endpoint detection in the case of the seriously changed noise environment, and it plays a very good foreshadowing role in the latter speech research.