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arXiv · 2206.10695

Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations

Detai Xin·Shinnosuke Takamichi·Hiroshi Saruwatari
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Abstract

We present an emotion recognition system for nonverbal vocalizations (NVs) submitted to the ExVo Few-Shot track of the ICML Expressive Vocalizations Competition 2022. The proposed method uses self-supervised learning (SSL) models to extract features from NVs and uses a classifier chain to model the label dependency between emotions. Experimental results demonstrate that the proposed method can significantly improve the performance of this task compared to several baseline methods. Our proposed method obtained a mean concordance correlation coefficient (CCC) of $0.725$ in the validation set and $0.739$ in the test set, while the best baseline method only obtained $0.554$ in the validation set. We publicate our code at https://github.com/Aria-K-Alethia/ExVo to help others to reproduce our experimental results.

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Detai Xin, Shinnosuke Takamichi, Hiroshi Saruwatari. 2022-06-21. Exploring the Effectiveness of Self-supervised Learning and Classifier Chains in Emotion Recognition of Nonverbal Vocalizations. https://arxiv.org/abs/2206.10695

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