EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition

• URL: http://arxiv.org/abs/2203.13617v2
• Date: Fri, 9 Jun 2023 14:45:18 GMT
• Title: EmotionNAS: Two-stream Neural Architecture Search for Speech Emotion Recognition
• Authors: Haiyang Sun, Zheng Lian, Bin Liu, Ying Li, Licai Sun, Cong Cai, Jianhua Tao, Meng Wang, Yuan Cheng
• Abstract summary: We propose a two-stream neural architecture search framework, called enquoteEmotionNAS. Specifically, we take two-stream features (i.e., handcrafted and deep features) as the inputs, followed by NAS to search for the optimal structure for each stream. Experimental results demonstrate that our method outperforms existing manually-designed and NAS-based models.
• Score: 48.71010404625924
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speech emotion recognition (SER) is an important research topic in human-computer interaction. Existing works mainly rely on human expertise to design models. Despite their success, different datasets often require distinct structures and hyperparameters. Searching for an optimal model for each dataset is time-consuming and labor-intensive. To address this problem, we propose a two-stream neural architecture search (NAS) based framework, called \enquote{EmotionNAS}. Specifically, we take two-stream features (i.e., handcrafted and deep features) as the inputs, followed by NAS to search for the optimal structure for each stream. Furthermore, we incorporate complementary information in different streams through an efficient information supplement module. Experimental results demonstrate that our method outperforms existing manually-designed and NAS-based models, setting the new state-of-the-art record.

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