基于自适应门限的分形维数语音端点检测  

Speech Endpoint Detection Based on Fractal Dimension with Adaptive Threshold

作　　者：郑艳[1] 高爽 ZHENG Yan;GAO Shuang(School of Information Science&Engineering,Northeastern University,Shenyang 110819,China)

机构地区：[1]东北大学信息科学与工程学院

出　　处：《东北大学学报（自然科学版）》2020年第1期7-11,共5页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(61773108)

摘　　要：针对固定门限方法在语音端点检测技术中的局限性,为了提高低信噪比下语音端点检测的鲁棒性和准确率,将自适应门限应用于分形维数的语音检测中,提出了一种新的语音端点检测算法.该算法通过对语音信号产生机制的分析,将分形维数用于语音起止点的检测中,设计了自适应门限,从而有效降低了噪声干扰对检测结果的影响,并实现了实时检测.仿真实验结果表明,在低信噪比的情况下,改进的端点检测算法比传统的短时能量检测算法可更准确有效地实现带噪语音的端点检测,而且对噪声干扰具有更好的鲁棒性.Considering the limitation of fixed threshold method in speech endpoint detection,in order to improve the robustness and accuracy of speech endpoint detection under low SNR(signal noise ratio),a novel speech detection algorithm was proposed based on adaptive threshold in fractal dimension.By analyzing the mechanism of speech signal generation,the fractal dimension was applied to the detection of speech starting and ending points,and an adaptive threshold was designed to avoid noise interference and to achieve real-time detection.The simulation results show that,compared with the traditional short-term energy detection algorithm,the proposed algorithm can effectively realize the endpoint detection of noisy speech under the low SNR,and has better robustness to noise interference.

关 键 词：语音端点检测 分形维数 自适应门限 低信噪比 鲁棒性 

分 类 号：TN912.3[电子电信—通信与信息系统]