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首页> 外文会议>International Conference on Affective Computing and Intelligent Interaction >Learning speech emotion features by joint disentangling-discrimination
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Learning speech emotion features by joint disentangling-discrimination

机译：通过联合解解歧视学习语音情感特征

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摘要

Speech plays an important part in human-computer interaction. As a major branch of speech processing, speech emotion recognition (SER) has drawn much attention of researchers. Excellent discriminant features are of great importance in SER. However, emotion-specific features are commonly mixed with some other features. In this paper, we introduce an approach to pull apart these two parts of features as much as possible. First we employ an unsupervised feature learning framework to achieve some rough features. Then these rough features are further fed into a semi-supervised feature learning framework. In this phase, efforts are made to disentangle the emotion-specific features and some other features by using a novel loss function, which combines reconstruction penalty, orthogonal penalty, discriminative penalty and verification penalty. Orthogonal penalty is utilized to disentangle emotion-specific features and other features. The discriminative penalty enlarges inter-emotion variations, while the verification penalty reduces the intra-emotion variations. Evaluations on the FAU Aibo emotion database show that our approach can improve the speech emotion classification performance.
机译：演讲在人机交互中起重要作用。作为语音处理的主要分支，语音情感认可（SER）已经引起了研究人员的关注。卓越的判别特征在Ser中具有重要意义。但是，情感特征通常与其他一些功能混合。在本文中，我们介绍了一种尽可能地拉开这两部分特征的方法。首先，我们聘请了一个无人监督的特征学习框架来实现一些粗略的功能。然后，这些粗略的特征进一步进入半监督特征学习框架。在这个阶段中，做出努力通过使用一种新颖的损失函数，它结合了重建惩罚，惩罚正交，判别罚分和验证罚解开情绪具体特征和一些其它特征。正交惩罚用于解散特定情绪的特征和其他功能。歧视性惩罚扩大了情绪间变异，而验证处罚减少了情绪内变化。对FAU AIBO情感数据库的评估表明，我们的方法可以提高语音情绪分类性能。

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