Abstract:
Cross-corpus speech emotion recognition (SER) plays a vital role in numerous practical applications. Traditional approaches to cross-corpus emotion transfer often concent...Show MoreMetadata
Abstract:
Cross-corpus speech emotion recognition (SER) plays a vital role in numerous practical applications. Traditional approaches to cross-corpus emotion transfer often concentrate on adapting acoustic features to align with different corpora, domains, or labels. However, acoustic features are inherently variable and error-prone due to factors like speaker differences, domain shifts, and recording conditions. To address these challenges, this study adopts a novel contrastive approach by focusing on emotion-specific articulatory gestures as the core elements for analysis. By shifting the emphasis on the more stable and consistent articulatory gestures, we aim to enhance emotion transfer learning in SER tasks. Our research leverages the CREMA-D and MSP-IMPROV corpora as benchmarks and it reveals valuable insights into the commonality and reliability of these articulatory gestures. The findings highlight mouth articulatory gesture potential as a better constraint for improving emotion recognition across different settings or domains.
Published in: ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Date of Conference: 06-11 April 2025
Date Added to IEEE Xplore: 07 March 2025
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Department of Electrical Engineering, National Tsing Hua University, Taiwan
Department of Electrical and Computer Engineering, University of Texas at Dallas, USA
Department of Electrical and Computer Engineering, University of Texas at Dallas, USA
Language Technologies Institute, Carnegie Mellon University, USA
Department of Electrical Engineering, National Tsing Hua University, Taiwan
Department of Electrical Engineering, National Tsing Hua University, Taiwan
Department of Electrical and Computer Engineering, University of Texas at Dallas, USA
Department of Electrical and Computer Engineering, University of Texas at Dallas, USA
Language Technologies Institute, Carnegie Mellon University, USA
Department of Electrical Engineering, National Tsing Hua University, Taiwan