高脉冲噪声坏境中双门限法语音端点检测研究

语音端点检测是对有效语音段的识别关键技术,准确的端点检测使语音信号的后续处理计算量减少,有效地节约资源。现在多数语音端点检测技术例如能频值、谱熵、小波能量熵变换等都能准确检测出有效的语音段。文中介绍了一种双门限端点检测法,即利用短时平均过零率和短时平均能量法进行双门限检测,再设置一个最短时间门限,有效地在高脉冲噪声环境中准确识别汉语发音。通过与其他方法对比实验,文中双门限技术在短时高脉冲噪声环境下能有效提高语音识别率。仿真结果表明,端点检测正确率达93%。

Speech Endpoint Detection of Double-Thresholding with High Impulse Noise

Endpoint detection of speech signals is crucial in speech recognition.Accurate endpoint detection can reduce the amount of computation of subsequent processing of speech signals and save system resources.Now many methods of the detection,such as instantaneous energy frequency value,spectral entropy and wavelet energy entropy,can accurately and effectively detect voice segments.This paper introduces a improved method for dual-threshold endpoint detection using short-time average zero-crossing rate and short-time average energy,then seting up a shortest timely restricted value to effectively detect Chinese speech in the high-pulse noise condition.A comparison with other methods shows that our method can effectively improve the recognition rate with high-pulse noise.Simulation results show that an accuracy of as high as 93% for endpoint detection can be achieved.