mlsanityData to 7 Oct 2026

Federated Acoustic Modeling For Automatic Speech Recognition

arXiv 2102.04429 ↗First posted 8 Feb 2021cs.SD cs.DC eess.AScited by 20

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Abstract (arXiv)

Data privacy and protection is a crucial issue for any automatic speech recognition (ASR) service provider when dealing with clients. In this paper, we investigate federated acoustic modeling using data from multiple clients. A client's data is stored on a local data server and the clients communicate only model parameters with a central server, and not their data. The communication happens infrequently to reduce the communication cost. To mitigate the non-iid issue, client adaptive federated training (CAFT) is proposed to canonicalize data across clients. The experiments are carried out on 1,150 hours of speech data from multiple domains. Hybrid LSTM acoustic models are trained via federated learning and their performance is compared to traditional centralized acoustic model training. The experimental results demonstrate the effectiveness of the proposed federated acoustic modeling strategy. We also show that CAFT can further improve the performance of the federated acoustic model.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Feb 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 4; 2022: 9; 2023: 4; 2024: 1; 2025: 1; 2026: 1.

20 papers.

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  1. 2024Automatic Speech Recognition using Advanced Deep Learning Approaches: A surveycited by 50
  2. 2023Backdoor Attacks and Defenses in Federated Learning: Survey, Challenges and Future Research Directionscited by 33
  3. 2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
  4. 2022A Review of Speech-centric Trustworthy Machine Learning: Privacy, Safety, and Fairnesscited by 13
  5. 2021Privacy attacks for automatic speech recognition acoustic models in a federated learning frameworkcited by 10
  6. 2022Decoupled Federated Learning for ASR with Non-IID Datacited by 6
  7. 2022Federated Self-Supervised Learning for Acoustic Event Classificationcited by 5
  8. 2022FedNST: Federated Noisy Student Training for Automatic Speech Recognitioncited by 5
  9. 2023Personalized Federated Learning on Long-Tailed Data via Adversarial Feature Augmentationcited by 5
  10. 2021Private Language Model Adaptation for Speech Recognitioncited by 4
  11. 2022Avoid Overfitting User Specific Information in Federated Keyword Spottingcited by 4
  12. 2022ILASR: Privacy-Preserving Incremental Learning for Automatic Speech Recognition at Production Scalecited by 4
8 more
  1. 2021Retrieving Speaker Information from Personalized Acoustic Models for Speech Recognitioncited by 2
  2. 2022Adaptive Multi-Corpora Language Model Training for Speech Recognitioncited by 2
  3. 2022Group Personalized Federated Learningcited by 1
  4. 2022An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognitioncited by 1
  5. 2023D4AM: A General Denoising Framework for Downstream Acoustic Modelscited by 1
  6. 2025Regularized Federated Learning for Privacy-Preserving Dysarthric and Elderly Speech Recognitioncited by 1
  7. 2023Ed-Fed: A generic federated learning framework with resource-aware client selection for edge devicescited by 0
  8. 2026Towards Personalized Federated Learning for Dysarthric Speech Recognitioncited by 0