Neural Architecture Search for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2203.16928v1
• Date: Thu, 31 Mar 2022 10:16:10 GMT
• Title: Neural Architecture Search for Speech Emotion Recognition
• Authors: Xixin Wu, Shoukang Hu, Zhiyong Wu, Xunying Liu, Helen Meng
• Abstract summary: We propose to apply neural architecture search (NAS) techniques to automatically configure the SER models. We show that NAS can improve SER performance (54.89% to 56.28%) while maintaining model parameter sizes.
• Score: 72.1966266171951
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have brought significant advancements to speech emotion recognition (SER). However, the architecture design in SER is mainly based on expert knowledge and empirical (trial-and-error) evaluations, which is time-consuming and resource intensive. In this paper, we propose to apply neural architecture search (NAS) techniques to automatically configure the SER models. To accelerate the candidate architecture optimization, we propose a uniform path dropout strategy to encourage all candidate architecture operations to be equally optimized. Experimental results of two different neural structures on IEMOCAP show that NAS can improve SER performance (54.89\% to 56.28\%) while maintaining model parameter sizes. The proposed dropout strategy also shows superiority over the previous approaches.

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