基于局部特征优化的语音情感识别

doi:10.7523/j.issn.2095-6134.2017.04.004

中国科学院大学学报 ›› 2017, Vol. 34 ›› Issue (4): 431-438.DOI: 10.7523/j.issn.2095-6134.2017.04.004

基于局部特征优化的语音情感识别

隋小芸^1,2, 朱廷劭¹, 汪静莹^1,2

1. 中国科学院心理研究所, 北京 100101;
2. 中国科学院大学心理学系, 北京 100049

收稿日期:2016-03-29 修回日期:2016-06-17 发布日期:2017-07-15
通讯作者: 朱廷劭,E-mail:tszhu@psych.ac.cn
基金资助:
国家重点基础研究发展（973）计划（2014CB744600）资助

Sample optimization based on local features in speech emotion recognition

SUI Xiaoyun^1,2, ZHU Tingshao¹, WANG Jingying^1,2

1. Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China;
2. Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China

Received:2016-03-29 Revised:2016-06-17 Published:2017-07-15

摘要/Abstract

摘要： 情感识别在人机交互领域具有广阔前景。由于情感表达在时间上具有一定的持续性，统计特征更能体现不同情绪语音的差异和动态变化，大多数语音情感识别研究都使用全局特征（如最大值、最小值等），并没有充分挖掘局部特征（如单帧的短时能量、过零率等）中的信息。提出一种基于局部特征优化的方法，对每个情感语音样本做进一步提纯，通过聚类分析对情感特征相对不显著的帧进行过滤，在此基础上进行统计计算和分类，以提高预测的准确率。实验结果表明，基于优化后的样本进行情感分类，3个语料库的平均准确率提高5%~17%。进一步的研究发现这种优化方法可能更适合于语音长度较长的情感识别场景。

关键词: 语音情感识别, 局部特征, 全局特征, 聚类分析, 数据优化

Abstract: Emotion recognition is one of the most prospective technics in human-machine interaction process. Most researches prefer statistical functional features because these features are more consistent with the speech variation as emotion changes. However, local features, i.e., short-term or temporal features extracted from single frame also contain useful information. In this work, a new approach is proposed to optimize samples via local features. To achieve this, a K-means cluster is employed to cluster each sample with 2 groups: frames with obvious emotion and frames with emotion which is not that obvious. It is hypothesized that the cluster with more frames should be emotionally obvious. It is observed in the results that the classification performs better on optimized samples than on original ones. The method was tested on 3 corpora and the classification accuracy increases by 5%-17%. It is also found the improvement increases as speech length grows, which implies the optimization approach may be more applicable to the longer speech recognition.

Key words: speech emotion recognition, local features, global features, cluster analysis, sample optimization

中图分类号:

TN391.4

隋小芸, 朱廷劭, 汪静莹. 基于局部特征优化的语音情感识别[J]. 中国科学院大学学报, 2017, 34(4): 431-438.

SUI Xiaoyun, ZHU Tingshao, WANG Jingying. Sample optimization based on local features in speech emotion recognition[J]. , 2017, 34(4): 431-438.

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基于局部特征优化的语音情感识别

Sample optimization based on local features in speech emotion recognition

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