文章摘要
卜玉婷,曾庆宁,郑展恒.一种低信噪比环境下的语音端点检测算法[J].声学技术,2020,39(5):592~602
一种低信噪比环境下的语音端点检测算法
A speech endpoint detection method in low SNR environment
投稿时间:2019-06-24  修订日期:2019-09-08
DOI:10.16300/j.cnki.1000-3630.2020.05.012
中文关键词: 低信噪比  瞬态抑制  调制域  功率归一化倒谱系数  倒谱距离  端点检测
英文关键词: low signal-to-noise ratio (SNR)  transient suppression  modulation domain  power normalized cepstrum coefficient  cepstrum distance  endpoint detection
基金项目:广西自然科学基金重点项目(2016GXNSFDA380018)、国家自然科学基金项目(61461011)、教育部重点实验室主任基金项目(CRKL160107)
作者单位E-mail
卜玉婷 桂林电子科技大学"认知无线电与信息处理"教育部重点实验室, 广西桂林 541004  
曾庆宁 桂林电子科技大学"认知无线电与信息处理"教育部重点实验室, 广西桂林 541004  
郑展恒 桂林电子科技大学"认知无线电与信息处理"教育部重点实验室, 广西桂林 541004 glzzh@guet.edu.cn 
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中文摘要:
      端点检测技术是语音信号处理的关键技术之一,为提高低信噪比环境下端点检测的准确率和稳健性,提出了一种非平稳噪声抑制和调制域谱减结合功率归一化倒谱距离的端点检测算法。该算法首先通过抑制非平稳噪声再采用调制域谱减消除残余噪声来提升信噪比,减少语音失真。然后再提取每帧信号的功率归一化倒谱系数,计算每帧信号与背景噪声的功率归一化倒谱距离。最后将该倒谱距离作为检测参数,采用双门限判决方法进行端点检测。实验结果表明,该端点检测算法对语音帧和噪声帧具有较好的区分性。此外,在低信噪比环境下,所提出的算法对于不同类型的噪声都具有较好的稳健性。
英文摘要:
      Endpoint detection technique is one of the key techniques in speech signal processing. In order to improve the accuracy and robustness of endpoint detection in low signal-to-noise ratio (SNR) environment, an endpoint detection algorithm based on non-stationary noise suppression and modulation domain spectral subtraction combining with power normalized cepstrum distance is proposed. Firstly, the algorithm suppresses non-stationary noise and uses modulation domain spectral subtraction to eliminate residual noise, so as to improve signal-to-noise ratio and reduce speech distortion. Then, the power normalized cepstrum coefficients of each frame signal are extracted. By calculating the power normalized cepstrum distance between each frame signal and background noise, a robust endpoint detection parameter is obtained. Finally, the double threshold method is used to perform endpoint detection by using this parameter. The experimental results show that the speech frames and noise frames can be effectively distinguished by endpoint detection algorithm. Furthermore, the proposed method achieves better anti-noise robustness for different types of noises even in a low SNR environment.
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