摘要
为了有效利用非文字音频事件进行会话语音的语义分析,在分析口语中频繁出现的音频事件特征差异的基础上,提出了一种基于两步判决的口语中非文字音频事件的检测方法.该方法利用音频事件的信号特征构造音频事件信号段,采用门限判决来检测长掌声(第一步判决),而用统计模型来检测其它音频事件(第二步判决).实验结果表明:该方法检测填音、笑声、掌声3种非文字音频事件的平均准确率、召回率和F1度量值分别为87.3%、93.8%和90.4%;与现有文献数据相比,F1度量值平均提高了7.5%,且文中方法能更精确地确定非文字音频事件的边界.
In order to effectively utilize non-lexical audio events to analyze the semantics of conversational speech,the characteristic differences among the audio events frequently occurring in spontaneous vocalization are analyzed,and a two-stage decision-based method to detect non-lexical audio events in spontaneous vocalization is proposed.In this method,the characteristics of audio events are used to construct signal segments of audio events： the thre-shold decision is used to detect longer applause（the first-stage decision）,and statistical models are employed to detect other audio events（the second-stage decision）.Experimental results show that the average precision,the recall rate and the F1-measure of the proposed method for three non-lexical audio events（i.e.filled pause,laughter and applause） are respectively 87.3%,93.8% and 90.4%;and that,as compared with the existing method,the proposed method is of an average F1-measure increase by 7.5% and it helps to determine the boundaries of non-lexical audio events with higher accuracy.
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2011年第2期20-25,31,共7页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(60972132)
广东省自然科学基金资助项目(10451064101004651
9351064101000003)
关键词
非文字音频事件
门限判决
统计模型检测
口语语音
语音处理
non-lexical audio event
threshold decision
statistical model detection
spontaneous speech
speech processing
作者简介
作者简介：贺前华（1965-），男，教授，博士生导师，主要从事语音及音频信号处理、嵌入式系统研究．E-mail：eeqhhe@scutedu．cn
