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Neural Architecture Search for Speech Emotion Recognition

Year: 2022

Type: preprint

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.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2203.16928

Authors: Wu, Xixin, Hu, Shoukang, Wu, Zhiyong, Liu, Xunying, Meng, Helen

Institutions: Chinese University of Hong Kong, Tsinghua Shenzhen International Graduate School, Tsinghua University

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Chinese University of Hong Kong

Award: G2603945996

OpenAlex ID: W4297806653

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