mlsanityData to 7 Oct 2026

Contrastive Unsupervised Learning for Speech Emotion Recognition

arXiv 2102.06357 ↗First posted 12 Feb 2021cs.SD cs.LG eess.AScited by 18

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Abstract (arXiv)

Speech emotion recognition (SER) is a key technology to enable more natural human-machine communication. However, SER has long suffered from a lack of public large-scale labeled datasets. To circumvent this problem, we investigate how unsupervised representation learning on unlabeled datasets can benefit SER. We show that the contrastive predictive coding (CPC) method can learn salient representations from unlabeled datasets, which improves emotion recognition performance. In our experiments, this method achieved state-of-the-art concordance correlation coefficient (CCC) performance for all emotion primitives (activation, valence, and dominance) on IEMOCAP. Additionally, on the MSP- Podcast dataset, our method obtained considerable performance improvements compared to baselines.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). Builds on 8 of its 22 references that are in the map. First posted on arXiv 12 Feb 2021.

Builds on

8 of 22 references.

References · 8

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Cited by year

Citing papers per year: 2021: 3; 2022: 6; 2023: 3; 2024: 2; 2025: 4.

18 papers.

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  1. 2021Emotion Recognition from Speech Using Wav2vec 2.0 Embeddingscited by 158
  2. 2022Dawn of the transformer era in speech emotion recognition: closing the valence gapcited by 155
  3. 2021SLUE: New Benchmark Tasks for Spoken Language Understanding Evaluation on Natural Speechcited by 70
  4. 2023ADCNet: Learning from Raw Radar Data via Distillationcited by 14
  5. 2022Probing Speech Emotion Recognition Transformers for Linguistic Knowledgecited by 13
  6. 2023Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognitioncited by 12
  7. 2024A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learningcited by 12
  8. 2021Multistage linguistic conditioning of convolutional layers for speech emotion recognitioncited by 8
  9. 2022Sentiment-Aware Automatic Speech Recognition pre-training for enhanced Speech Emotion Recognitioncited by 8
  10. 2022Supervised Contrastive Learning for Affect Modellingcited by 8
  11. 2024Revealing Emotional Clusters in Speaker Embeddings: A Contrastive Learning Strategy for Speech Emotion Recognitioncited by 8
  12. 2022Multimodal Emotion Recognition with Modality-Pairwise Unsupervised Contrastive Losscited by 7
6 more
  1. 2023Noise robust speech emotion recognition with signal-to-noise ratio adapting speech enhancementcited by 5
  2. 2022Speech Emotion Recognition via an Attentive Time-Frequency Neural Networkcited by 3
  3. 2025Privileged Contrastive Pretraining for Multimodal Affect Modellingcited by 3
  4. 2025Learning More with Less: Self-Supervised Approaches for Low-Resource Speech Emotion Recognitioncited by 2
  5. 2025Leveraging Cross-Attention Transformer and Multi-Feature Fusion for Cross-Linguistic Speech Emotion Recognitioncited by 0
  6. 2025Emotion-Disentangled Embedding Alignment for Noise-Robust and Cross-Corpus Speech Emotion Recognitioncited by 0