Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition

• URL: http://arxiv.org/abs/2012.05429v2
• Date: Sat, 12 Dec 2020 14:59:33 GMT
• Title: Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition
• Authors: Ying Zhou, Xuefeng Liang, Yu Gu, Yifei Yin, Longshan Yao
• Abstract summary: We propose a novel multi-classifier interactive learning (MCIL) method to address the ambiguous speech emotions. MCIL mimics several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels. Experiments show that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.
• Score: 9.856709988128515
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attention in both industry and academic. Since emotions existing in an entire utterance may have varied probabilities, speech emotion is likely to be ambiguous, which poses great challenges to recognition tasks. However, previous studies commonly assigned a single-label or multi-label to each utterance in certain. Therefore, their algorithms result in low accuracies because of the inappropriate representation. Inspired by the optimally interacting theory, we address the ambiguous speech emotions by proposing a novel multi-classifier interactive learning (MCIL) method. In MCIL, multiple different classifiers first mimic several individuals, who have inconsistent cognitions of ambiguous emotions, and construct new ambiguous labels (the emotion probability distribution). Then, they are retrained with the new labels to interact with their cognitions. This procedure enables each classifier to learn better representations of ambiguous data from others, and further improves the recognition ability. The experiments on three benchmark corpora (MAS, IEMOCAP, and FAU-AIBO) demonstrate that MCIL does not only improve each classifier's performance, but also raises their recognition consistency from moderate to substantial.

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