mlsanityData to 14 Oct 2026

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

arXiv 2506.02059 ↗First posted 1 Jun 2025cs.SD cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 18 of its 38 references that are in the map. First posted on arXiv 1 Jun 2025.

Builds on

18 of 38 references.

References · 18

  1. 2020Bootstrap your own latent: A new approach to self-supervised Learningcited by 4,750
  2. 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 4,057
  3. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  4. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  5. 2019SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognitioncited by 1,380
  6. 2020ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verificationcited by 628
  7. 2021BYOL for Audio: Self-Supervised Learning for General-Purpose Audio Representationcited by 106
  8. 2023Multimodal Multi-loss Fusion Network for Sentiment Analysiscited by 27
  9. 2024Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuancescited by 27
  10. 2018Cross Lingual Speech Emotion Recognition: Urdu vs. Western Languagescited by 25
  11. 2021Contrastive Unsupervised Learning for Speech Emotion Recognitioncited by 19
  12. 2025Multimodal Emotion Recognition in Conversations: A Survey of Methods, Trends, Challenges and Prospectscited by 11
6 more
  1. 2024Improving Speech Emotion Recognition in Under-Resourced Languages via Speech-to-Speech Translation with Bootstrapping Data Selectioncited by 10
  2. 2024Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognitioncited by 8
  3. 2023Deep Implicit Distribution Alignment Networks for Cross-Corpus Speech Emotion Recognitioncited by 3
  4. 2023A Comparative Study of Pre-trained Speech and Audio Embeddings for Speech Emotion Recognitioncited by 3
  5. 2020A New Approach to Accent Recognition and Conversion for Mandarin Chinesecited by 2
  6. 2023Towards Generalizable SER: Soft Labeling and Data Augmentation for Modeling Temporal Emotion Shifts in Large-Scale Multilingual Speechcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026PTS-SNN: A Prompt-Tuned Temporal Shift Spiking Neural Networks for Efficient Speech Emotion Recognitioncited by 1
  2. 2026SURE: Synergistic Uncertainty-aware Reasoning for Multimodal Emotion Recognition in Conversationscited by 0