Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search

• URL: http://arxiv.org/abs/2211.08237v2
• Date: Wed, 16 Nov 2022 01:47:34 GMT
• Title: Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search
• Authors: Zihan Wang, Qi Meng, HaiFeng Lan, XinRui Zhang, KeHao Guo, Akshat Gupta
• Abstract summary: Speech emotion recognition (SER) classifies audio into emotion categories such as Happy, Angry, Fear, Disgust and Neutral. This paper proposes a language-specific model that extract emotional information from multiple pre-trained speech models. Our model raises the state-of-the-art accuracy by 3% for German and 14.3% for French.
• Score: 15.51730246937201
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech emotion recognition (SER) classifies audio into emotion categories such as Happy, Angry, Fear, Disgust and Neutral. While Speech Emotion Recognition (SER) is a common application for popular languages, it continues to be a problem for low-resourced languages, i.e., languages with no pretrained speech-to-text recognition models. This paper firstly proposes a language-specific model that extract emotional information from multiple pre-trained speech models, and then designs a multi-domain model that simultaneously performs SER for various languages. Our multidomain model employs a multi-gating mechanism to generate unique weighted feature combination for each language, and also searches for specific neural network structure for each language through a neural architecture search module. In addition, we introduce a contrastive auxiliary loss to build more separable representations for audio data. Our experiments show that our model raises the state-of-the-art accuracy by 3% for German and 14.3% for French.

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