基于特征流融合的带噪语音检测算法

龙华; 杨明亮; 邵玉斌

doi:10.11959/j.issn.1000-436x.2020067

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基于特征流融合的带噪语音检测算法

学术论文 | 更新时间：2024-06-05

- 基于特征流融合的带噪语音检测算法
- Noisy voice detection algorithm based on feature stream fusion
- 通信学报 2020年41卷第4期页码：134-142
- 作者机构：
  
  昆明理工大学信息工程与自动化学院，云南昆明 650031
- 作者简介：
  
  [ "龙华（1963- ），女，回族，云南大理人，博士，昆明理工大学教授，主要研究方向为无线网络及音频信号处理" ]
  [ "杨明亮（1994- ），男，四川宜宾人，昆明理工大学硕士生，主要研究方向为音频信号处理、语音识别" ]
  [ "邵玉斌（1970- ），男，云南曲靖人，昆明理工大学教授，主要研究方向为移动通信和个人通信系统以及信号处理" ]
- 基金信息：
  
  国家自然科学基金资助项目(61761025)
- DOI：10.11959/j.issn.1000-436x.2020067
  中图分类号： TP391.42
- 网络出版日期：2020-04，
  
  纸质出版日期：2020-04-25
- 稿件说明：
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龙华, 杨明亮, 邵玉斌. 基于特征流融合的带噪语音检测算法[J]. 通信学报, 2020,41(4):134-142.

Hua LONG, Mingliang YANG, Yubin SHAO. Noisy voice detection algorithm based on feature stream fusion[J]. Journal on communications, 2020, 41(4): 134-142.
龙华, 杨明亮, 邵玉斌. 基于特征流融合的带噪语音检测算法[J]. 通信学报, 2020,41(4):134-142. DOI： 10.11959/j.issn.1000-436x.2020067.

Hua LONG, Mingliang YANG, Yubin SHAO. Noisy voice detection algorithm based on feature stream fusion[J]. Journal on communications, 2020, 41(4): 134-142. DOI： 10.11959/j.issn.1000-436x.2020067.

摘要

针对语音通话中语音段的起始检测性能不佳，检测语音连续性结构受到破坏的问题，提出了一种基于特征流融合的带噪语音检测算法。首先，根据语音特性分别提取时域特征流、谱图特征流和统计特征流；其次，利用不同的语音特征流分别对带噪音频中的语音段进行概率估测；最后，将各个特征流估测得到的语音估测概率进行加权融合，并利用隐马尔可夫模型对语音估测概率进行短时状态处理。通过对复合语音数据库在多类型噪声与不同信噪比条件下的性能测试表明，所提算法相对于基于贝叶斯与 DNN 分类器的基线模型相比，语音检测正确率分别提高了21.26%与11.01%，显著提高了目标语音的质量。

Abstract

Aiming at the problem that the initial detection performance of voice segment was poor

and the voice continuity structure was damaged in voice communication

a noisy voice detection algorithm based on feature stream fusion was proposed.Firstly

the time domain feature stream

the spectral pattern feature stream and the statistical feature stream were extracted according to the voice characteristics.Secondly

the voice segment in the noisy audio was estimated by different voice feature streams.Finally

the voice prediction probability obtained by each feature stream was weighted and fused

and the voice estimation probability was processed in short time by the hidden Markov model.The performance test of composite voice database under the condition of multi-type noise and different signal-to-noise ratio shows that compared with the baseline model based on Bayesian and DNN classifier

the voice detection accuracy of the proposed algorithm is improved by 21.26% and 11.01% respectively

and the quality of target voice is significantly improved.

关键词

Keywords

references

WEI J , SUN X . Research on speech endpoint detection algorithm with low SNR [J ] . Open Access Library Journal , 2017 , 4 ( 3 ): 1 - 8 .

LI Q , XIE H E , ZHENG Q J , et al . The voice activity detection algorithm based on spectral entropy and high-order statistics [J ] . Applied Mechanics and Materials , 2014 ( 624 ): 495 - 499 .

PALIWAL K K , WOJCICKI K , SCHWERIN B , et al . Single-channe speech enhancement using spectral subtraction in the short-time modulation domain [J ] . Speech Communication , 2010 , 52 ( 5 ): 450 - 475 .

MENDELEV V , PRISYACH T , PRUDNIKOV A , et al . Robust voice activity detection with deep maxout neural networks [J ] . Mathematical Models and Methods in Applied Sciences , 2015 , 9 ( 8 ): 153 - 159 .

LENGERICH C , HANNUN A . An end-to-end architecture for keyword spotting and voice activity detection [J ] . arXiv Preprint,arXiv:1611.09405 , 2016

IVRY A , BERDUGO B , COHEN I , et al . Voice activity detection for transient noisy environment based on diffusion nets [J ] . IEEE Journal of Selected Topics in Signal Processing , 2019 , 13 ( 2 ): 254 - 264 .

SOHN J , KIM N S , SUNG W , et al . A statistical model-based voice activity detection [J ] . IEEE Signal Processing Letters , 1999 , 6 ( 1 ): 1 - 3 .

FISHER E , TABRIKIAN J , DUBNOV S , et al . Generalized likelihood ratio test for voiced-unvoiced decision in noisy speech using the harmonic model [J ] . IEEE Transactions on Audio,Speech,and Language Processing , 2006 , 14 ( 2 ): 502 - 510 .

KIM J T , JUNG S H , CHO K H , et al . Efficient harmonic peak detection of vowel sounds for enhanced voice activity detection [J ] . Let Signal Processing , 2018 , 12 ( 8 ): 975 - 982 .

SHAMMA S A , ELHILALI M , MICHEYL C , et al . Temporal coherence and attention in auditory scene analysis [J ] . Trends in Neurosciences , 2011 , 34 ( 3 ): 114 - 123 .

HWANG S , JIN Y G , SHIN J W , et al . Dual microphone voice activity detection based on reliable spatial cues [J ] . Sensors , 2019 , 19 ( 14 ): 3056 - 3064 .

TEKI S , BARASCUD N , PICARD S , et al . Neural correlates of auditory figure-ground segregation based on temporal coherence [J ] . Cerebral Cortex , 2016 , 26 ( 9 ): 3669 - 3680 .

KLEINSCHMIDT M , . Spectro-temporal Gabor features as a front end for automatic speech recognition [C ] // In 3rd European Congress on Acoustics.[S.n.:s.l] . 2002 : 1 - 6 .

罗智勇 , 杨旭 , 孙广路 , 等 . 基于马尔可夫的有限自动机入侵容忍系统模型 [J ] . 通信学报 , 2019 , 40 ( 10 ): 79 - 89 .

LUO Z Y , YANG X , SUN G L , et al . Finite automaton intrusion tolerance system model based on Markov [J ] . Journal on Communications , 2019 , 40 ( 10 ): 79 - 89 .

NIELSEN J K , JENSEN T L , JENSEN J R , et al . Fast fundamental frequency estimation:making a statistically efficient estimator computationally efficient [J ] . Signal Process , 2017 ( 135 ): 188 - 197 .

SHE L M , NIELSEN J K , JENSEN J R , et al . Bayesian pitch tracking based on the harmonic model [J ] . IEEE/ACM Transactions on Audio,Speech,and Language Processing , 2019 , 27 ( 11 ): 1737 - 1751 .

SCHLUTER R , BEZRUKOV L , WANGNER H , et al . Gammatone features and feature combination for large vocabulary speech recognition [C ] // International Conference on Acoustics,Speech and Signal Processing . Piscataway:IEEE Press , 2007 : 649 - 652 .

SHAO Y , JIN Z , WANG D L , et al . An auditory-based feature for robust speech recognition [C ] // Proceedings of International Conference on Acoustics,Speech and Signal Processing . Piscataway:IEEE Press , 2009 : 4625 - 4628 .

MAARTEN V S , ANDREAS T , NARAYANAN S . A robust front end for VAD:exploiting contextual,discriminative and spectral cues of human voice [C ] // Proceedings of the Annual Conference of the International Speech Communication Association . Piscataway:IEEE Press , 2013 : 704 - 708 .

MAARTEN V S , ANDREAS T ， NARAYANAN . A robust front end for VAD:exploiting contextual,discriminative and spectral cues of human voice [C ] // Proceedings of the Annual Conference of the International Speech Communication Association . Piscataway:IEEE Press , 2013 : 704 - 708 .

LU L , JIANG H , ZHANG H , et al . A robust audio classification and segmentation method [C ] // ACM Multimedia . New York:ACM Press , 2001 : 203 - 211 .

ZHAO J H , GAO H B , LIU Y C , et al . Speech recognition algorithm based on neural network and hidden Markov model [J ] . The Journal of China Universities of Posts and Telecommunications , 2018 , 25 ( 4 ): 28 - 37 .

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