[1]腾潇琦[],冯祥[],张翼飞[][]. 一种自适应建模的VAD方法[J].计算机技术与发展,2016,26(09):26-29.
　TENG Xiao-qi[],FENG Xiang[],ZHANG Yi-fei[][]. An Voice Activity Detection of Adaptive Modeling[J].,2016,26(09):26-29.
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 一种自适应建模的VAD方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
26
期数:
2016年09期
页码:
26-29
栏目:
应用开发研究
出版日期:
2016-09-10

文章信息/Info

Title:
 An Voice Activity Detection of Adaptive Modeling
文章编号:
1673-629X（2016)09-0026-04
作者:
 腾潇琦[1] ; 冯祥[2] ; 张翼飞[2][3]
1.北京市互联网信息办公室；2.讯飞智元信息科技有限公司；3.上海大学 机电工程与自动化学院
Author(s):
 TENG Xiao-qi[1] ; FENG Xiang[2] ; ZHANG Yi-fei[2][3]
关键词:
 ; 语音活动检测 ;  能量VAD;  模型VAD ; 自适应建模
Keywords:
 ; voice activity detection ; energy VAD ; model VAD ; adaptive modeling
分类号:
TP301
文献标志码:
A
摘要:
 语音活动检测( Voice Activity Detection,VAD)是语音前端特征处理的一个重要环节,它直接影响到后续处理的效果和效率。主流的模型VAD对训练数据的依赖度过高,在不同场景下需要重新训练不同的模型,这带来的数据标注的工作量是非常惊人的。一种自适应建模的VAD方法结合了能量VAD和模型VAD的优点,成功地解决了这个问题。它对每一条语音在线地训练出语音和非语音模型,根据每一帧在模型上的似然度得分给它们打上标签,经过平滑后就可以很好地找到语音的起点和终点。实验结果表明,该方法取得了很好的效果, F1指标相比传统能量VAD提升了0.031,说话人分离错误率下降了0.45%。
Abstract:
 Voice Activity Detection ( VAD) is an important part of speech front-end features processing which directly affects the effec-tiveness and efficiency of subsequent processing. Because of over-dependence on training data,the model VAD must train different model in different scenarios that will bring many tasks of data labeling. A VAD method of adaptive modeling,which combines with the advanta-ges of energy VAD and model VAD,solves the problem successfully. It trains speech model and non-speech model online to each voice and labels each frame according to the likelihood score of different model,then the endpoint of voice can be get. The experiments show thatthis method has achieved a good result. It makes the F1 parameters increased 0. 031 and error rate of speaker separation decreased by 0. 45% compared with the traditional energy VAD.

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更新日期/Last Update: 2016-10-24