计算机工程与应用Issue(20)：203-207,212,6.DOI:10.3778/j.issn.1002-8331.1501-0079
基于时频参数融合的自适应语音端点检测算法
Self-adaptive voice activity detection algorithm based on fusion of time-frequency para-meter
摘要
Abstract
In order to solve the inferior performance and sad self-adaptive of the traditional voice activity detection algo-rithm 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%.关键词
自适应/语音端点检测/Mel能量/时频参数Key words
self-adaptive/voice activity detection/Mel-scale log-energy/Time-Frequency(TF)parameter分类
信息技术与安全科学引用本文复制引用
王晓华,屈雷..基于时频参数融合的自适应语音端点检测算法[J].计算机工程与应用,2015,(20):203-207,212,6.基金项目
西安工程大学控制科学与工程学科建设经费资助（No.107090811）；国家自然科学基金（No.61301276）。 （）
