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arXiv · 2506.02059

Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition

Ziwei Gong·Pengyuan Shi·Kaan Donbekci·Lin Ai·Run Chen·David Sasu·Zehui Wu·Julia Hirschberg
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Abstract

Speech Emotion Recognition (SER) has seen significant progress with deep learning, yet remains challenging for Low-Resource Languages (LRLs) due to the scarcity of annotated data. In this work, we explore unsupervised learning to improve SER in low-resource settings. Specifically, we investigate contrastive learning (CL) and Bootstrap Your Own Latent (BYOL) as self-supervised approaches to enhance cross-lingual generalization. Our methods achieve notable F1 score improvements of 10.6% in Urdu, 15.2% in German, and 13.9% in Bangla, demonstrating their effectiveness in LRLs. Additionally, we analyze model behavior to provide insights on key factors influencing performance across languages, and also highlighting challenges in low-resource SER. This work provides a foundation for developing more inclusive, explainable, and robust emotion recognition systems for underrepresented languages.

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Ziwei Gong, Pengyuan Shi, Kaan Donbekci, Lin Ai, Run Chen, David Sasu, Zehui Wu, Julia Hirschberg. 2025-06-01. Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognition. https://arxiv.org/abs/2506.02059

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