mlsanityData to 7 Oct 2026

Semi-FedSER: Semi-supervised Learning for Speech Emotion Recognition On Federated Learning using Multiview Pseudo-Labeling

arXiv 2203.08810 ↗First posted 15 Mar 2022eess.AS cs.CR cs.LGcited by 12

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Abstract (arXiv)

Speech Emotion Recognition (SER) application is frequently associated with privacy concerns as it often acquires and transmits speech data at the client-side to remote cloud platforms for further processing. These speech data can reveal not only speech content and affective information but the speaker's identity, demographic traits, and health status. Federated learning (FL) is a distributed machine learning algorithm that coordinates clients to train a model collaboratively without sharing local data. This algorithm shows enormous potential for SER applications as sharing raw speech or speech features from a user's device is vulnerable to privacy attacks. However, a major challenge in FL is limited availability of high-quality labeled data samples. In this work, we propose a semi-supervised federated learning framework, Semi-FedSER, that utilizes both labeled and unlabeled data samples to address the challenge of limited labeled data samples in FL. We show that our Semi-FedSER can generate desired SER performance even when the local label rate l=20 using two SER benchmark datasets: IEMOCAP and MSP-Improv.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 17 of its 30 references that are in the map. First posted on arXiv 15 Mar 2022.

Builds on

17 of 30 references.

References · 17

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2020FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidencecited by 2,080
  3. 2019Deep Leakage from Gradientscited by 1,004
  4. 2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 652
  5. 2021In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learningcited by 301
  6. 2019Mockingjay: Unsupervised Speech Representation Learning with Deep Bidirectional Transformer Encoderscited by 196
  7. 2020TERA: Self-Supervised Learning of Transformer Encoder Representation for Speechcited by 186
  8. 2019An Unsupervised Autoregressive Model for Speech Representation Learningcited by 185
  9. 2021DistilHuBERT: Speech Representation Learning by Layer-wise Distillation of Hidden-unit BERTcited by 129
  10. 2020Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learningcited by 112
  11. 2020DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector Quantizationcited by 77
  12. 2020Non-Autoregressive Predictive Coding for Learning Speech Representations from Local Dependenciescited by 47
5 more
  1. 2021Multiview Pseudo-Labeling for Semi-supervised Learning from Videocited by 32
  2. 2021SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labelingcited by 29
  3. 2018Attention Based Fully Convolutional Network for Speech Emotion Recognitioncited by 23
  4. 2021Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settingscited by 12
  5. 2022Privacy-preserving Speech Emotion Recognition through Semi-Supervised Federated Learningcited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 2; 2025: 2; 2026: 2.

12 papers.

  1. 2023FedMultimodal: A Benchmark For Multimodal Federated Learningcited by 51
  2. 2022Federated Few-Shot Learning for Mobile NLPcited by 15
  3. 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
  4. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  5. 2023Federated learning for secure development of AI models for Parkinson's disease detection using speech from different languagescited by 9
  6. 2024Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-Labelingcited by 3
  7. 2024GMP-TL: Gender-augmented Multi-scale Pseudo-label Enhanced Transfer Learning for Speech Emotion Recognitioncited by 2
  8. 2025Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognitioncited by 1
  9. 2023Federated Representation Learning for Automatic Speech Recognitioncited by 0
  10. 2025Unified Alignment Protocol: Making Sense of the Unlabeled Data in New Domainscited by 0
  11. 2026Unrequited Emotions: Investigating the Gaps in Motivation and Practice in Speech Emotion Recognition Researchcited by 0
  12. 2026Towards Personalized Federated Learning for Dysarthric Speech Recognitioncited by 0