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Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selection

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arxiv 2409.10985 v2 pith:LPJSO4YM submitted 2024-09-17 eess.AS cs.CLcs.SD

Hsi-Che Lin , Yi-Cheng Lin , Huang-Cheng Chou , Hung-yi Lee This is my paper · ORCID
classification eess.AScs.CLcs.SD
keywords datalanguagesapproachbootstrappingemotionlabeledmultilingualrecognition
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Speech Emotion Recognition (SER) is a crucial component in developing general-purpose AI agents capable of natural human-computer interaction. However, building robust multilingual SER systems remains challenging due to the scarcity of labeled data in languages other than English and Chinese. In this paper, we propose an approach to enhance SER performance in low SER resource languages by leveraging data from high-resource languages. Specifically, we employ expressive Speech-to-Speech translation (S2ST) combined with a novel bootstrapping data selection pipeline to generate labeled data in the target language. Extensive experiments demonstrate that our method is both effective and generalizable across different upstream models and languages. Our results suggest that this approach can facilitate the development of more scalable and robust multilingual SER systems.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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

    cs.SD 2025-06 conditional novelty 5.0 of 10

    Adding speaker-contrastive or BYOL self-supervised pretraining to a Whisper-based model improves low-resource speech emotion recognition on Urdu, German, and Bangla.

  2. Mitigating Subgroup Disparities in Multi-Label Speech Emotion Recognition: A Pseudo-Labeling and Unsupervised Learning Approach

    eess.AS 2025-05 conditional novelty 4.0 of 10

    Guessing demographic groups from speech with pseudo-labels or k-means, then applying group-based debiasing, reduces reported gender fairness gaps in multi-label SER on CREMA-D, with accuracy losses the abstract understates.

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