Physiologically-inspired feature extraction for emotion recognition
Physiologically-inspired feature extraction for emotion recognition
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DOI：
10.21437/interspeech.2009-478
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发表时间：
2009
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影响因子：
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通讯作者：
Yu Zhou;Yanqing Sun;Junfeng Li;Jianping Zhang;Yonghong Yan
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文献类型：
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作者：
Yu Zhou;Yanqing Sun;Junfeng Li;Jianping Zhang;Yonghong Yan
基于情感产生的生理机制，提出了一种新的情感识别特征提取方法。据物理学家报道，情感语音与正常语音在发音器官方面的编码不同，并且语音中的情感信息集中在由器官的不同运动引起的不同频率[4]。为了应用这些发现，在本文中，我们首先量化的语音情感信息沿着分布沿每个频带利用Fisher的F比和互信息技术，然后提出了一种非均匀子带处理方法，这是能够提取和强调语音中的情感特征。这些提取的特征最终应用于情感识别。在语音情感识别中的实验结果表明，使用我们提出的非均匀子带处理提取的特征优于传统的（MFCC）特征，语音情感识别的平均错误减少率达16.8%。检索词：语音情感识别、特征提取、非均匀子带。
In this paper, we proposed a new feature extraction method for emotion recognition based on the knowledge of the emotion production mechanism in physiology. It was reported by physiacoustist that emotional speech is differently encoded from the normal speech in terms of articulation organs and that emotion information in speech is concentrated in different frequencies caused by the different movements of organs [4]. To apply these findings, in this paper, we first quantified the distribution of speech emotion information along with each frequency band by exploiting the Fisher’s F-Ratio and mutual information techniques, and then proposed a non-uniform sub-band processing method which is able to extract and emphasize the emotion features in speech. These extracted features are finally applied to emotional recognition. Experimental results in speech emotion recognition showed that the extracted features using our proposed non-uniform sub-band processing outperform the traditional (MFCC) features, and the average error reduction rate amounts to 16.8% for speech emotion recognition. Index Terms: speech emotion recognition, feature extraction, non-uniform sub-band.