社会与工程心理学研究室知识库

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。

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．