计算机应用与软件2017，Vol.34Issue(11)：91-96,6.DOI:10.3969/j.issn.1000-386x.2017.11.017
基于最大熵谱估计和时频特性的语音端点检测
SPEECH SIGNAL ENDPOINT DETECTION BASED ON MAXIMUM ENTROPY SPECTRUM ESTIMATION AND TIME-FREQUENCY SIGNATURE
陈莹莹 1简磊1
作者信息
- 1. 四川大学锦江学院电气与电子信息工程学院 四川彭山620860
- 折叠
摘要
Abstract
Speech endpoint detection is crucial to the construction of a practical automatic speech recognition system.A new algorithm based on the maximum entropy spectrum estimation and time-frequency signature is proposed to improve the performance of speech endpoint detection in low SNR (Signal Noise Ratio) environment.The framed speech signal power spectrum was estimated through the maximum entropy,and then the characteristics of noisy speech were extracted in time-frequency field in order to detect the endpoint.Experimental results show that,this method can accurately capture the characteristics of speech signals under lower SNR (-9 ～ 0 dB),and significantly improves the accuracy of endpoint detection.关键词
端点检测/最大熵谱估计/时频特性/信噪比Key words
Endpoint detection/Maximum entropy spectrum estimation/Time-frequency characteristics/SNR分类
信息技术与安全科学引用本文复制引用
陈莹莹,简磊..基于最大熵谱估计和时频特性的语音端点检测[J].计算机应用与软件,2017,34(11):91-96,6.
