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首页> 外文期刊>International journal of speech technology >Significance of Phonological Features in Speech Emotion Recognition
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Significance of Phonological Features in Speech Emotion Recognition

机译：语音情感识别中语音特征的意义

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A novel Speech Emotion Recognition (SER) method based on phonological features is proposed in this paper. Intuitively, as expert knowledge derived from linguistics, phonological features are correlated with emotions. However, it has been found that they are seldomly used as features to improve SER. Motivated by this, we set our goal to utilize phonological features to further advance SER's accuracy since they can provide complementary information for the task. Furthermore, we will also explore the relationship between phonological features and emotions. Firstly, instead of only based on acoustic features, we devise a new SER approach by fusing phonological representations and acoustic features together. A significant improvement in SER performance has been demonstrated on a publicly available SER database named Interactive Emotional Dyadic Motion Capture (IEMOCAP). Secondly, the experimental results show that the top-performing method for the task of categorical emotion recognition is a deep learning-based classifier which generates an unweighted average recall (UAR) accuracy of 60.02%. Finally, we investigate the most discriminative features and find some patterns of emotional rhyme based on the phonological representations.
机译：本文提出了一种基于语音特征的新型语音情感识别（SER）方法。直观地，作为来自语言学的专家知识，语音特征与情绪相关。然而，已经发现它们很少用作改善Ser的功能。通过此激励，我们将目标设为利用语音功能来进一步推进SER的准确性，因为它们可以为任务提供互补信息。此外，我们还将探索语音特征和情绪之间的关系。首先，而不是仅基于声学特征，我们通过融合音韵表示和声学特征来设计新的SER方法。已在名为交互式情绪二进制动作捕获（IEMocap）的公开的SER数据库上进行了显着改进。其次，实验结果表明，基于深入的学习的分类，实验结果表明，基于深度学习的分类器，它产生了60.02％的未加权平均召回（UAR）精度。最后，我们调查了最辨别性的特征，并根据语音表现找到一些情绪押韵模式。

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