Abstract: In order to solve the inferior performance and sad self-adaptive of the traditional voice activity detection algorithm in an environment with low Signal to Noise Ratio （SNR）, a new self-adaptive voice activity detection algorithm based on TF parameters is put forward. After introducing the time-domain log-energy and improved mel-scale energy, the new Time-Frequency （TF） parameters are acquired by coalescing them, which make it possible for distinguishing speech from noise effectively. Then, the TF parameters are updated to predicate endpoint through the threshold test. Simulation experiments show that the algorithm has better robustness and more precise detection. When the SNR is 0 dB, the error rate of the algorithm is about 15%.

Key words: self-adaptive, voice activity detection, Mel-scale log-energy, Time-Frequency（TF） parameter

摘要： 为了解决低信噪比环境下传统的语音端点检测算法性能较差且不能自适应环境噪声，提出了一种基于时频参数融合的自适应语音端点检测算法。将对数能量与改进的Mel能量进行融合，获得了一种新的时频参数（TF），该参数能有效地区分语音段和噪声段。使用该参数在噪声段对阈值进行更新，采用门限检测法判定出语音端点。仿真实验表明，该算法具有较好的鲁棒性，且能够准确地检测出语音端点。当信噪比（SNR）为0 dB时，端点检测错误率仅为15%左右。

关键词: 自适应, 语音端点检测, Mel能量, 时频参数