mlsanityData to 14 Oct 2026

Pseudo-Labeling for Domain-Agnostic Bangla Automatic Speech Recognition

arXiv 2311.03196 ↗First posted 6 Nov 2023cs.CL cs.AIcited by 4

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Abstract (arXiv)

One of the major challenges for developing automatic speech recognition (ASR) for low-resource languages is the limited access to labeled data with domain-specific variations. In this study, we propose a pseudo-labeling approach to develop a large-scale domain-agnostic ASR dataset. With the proposed methodology, we developed a 20k+ hours labeled Bangla speech dataset covering diverse topics, speaking styles, dialects, noisy environments, and conversational scenarios. We then exploited the developed corpus to design a conformer-based ASR system. We benchmarked the trained ASR with publicly available datasets and compared it with other available models. To investigate the efficacy, we designed and developed a human-annotated domain-agnostic test set composed of news, telephony, and conversational data among others. Our results demonstrate the efficacy of the model trained on psuedo-label data for the designed test-set along with publicly-available Bangla datasets. The experimental resources will be publicly available.(https://github.com/hishab-nlp/Pseudo-Labeling-for-Domain-Agnostic-Bangla-ASR)

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 31 references that are in the map. First posted on arXiv 6 Nov 2023.

Builds on

19 of 31 references.

References · 19

  1. 2020wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representationscited by 3,905
  2. 2021HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Unitscited by 2,342
  3. 2020Conformer: Convolution-augmented Transformer for Speech Recognitioncited by 1,863
  4. 2019Common Voice: A Massively-Multilingual Speech Corpuscited by 1,384
  5. 2022data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Languagecited by 727
  6. 2022FLEURS: Few-shot Learning Evaluation of Universal Representations of Speechcited by 469
  7. 2023Scaling Speech Technology to 1,000+ Languagescited by 411
  8. 2021W2v-BERT: Combining Contrastive Learning and Masked Language Modeling for Self-Supervised Speech Pre-Trainingcited by 347
  9. 2019Self-Training for End-to-End Speech Recognitioncited by 129
  10. 2023End-to-End Speech Recognition: A Surveycited by 129
  11. 2023Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognitioncited by 124
  12. 2020Iterative Pseudo-Labeling for Speech Recognitioncited by 83
7 more
  1. 2020Developing RNN-T Models Surpassing High-Performance Hybrid Models with Customization Capabilitycited by 63
  2. 2020SlimIPL: Language-Model-Free Iterative Pseudo-Labelingcited by 54
  3. 2021Towards One Model to Rule All: Multilingual Strategy for Dialectal Code-Switching Arabic ASRcited by 41
  4. 2021Kaizen: Continuously improving teacher using Exponential Moving Average for semi-supervised speech recognitioncited by 21
  5. 2023OOD-Speech: A Large Bengali Speech Recognition Dataset for Out-of-Distribution Benchmarkingcited by 9
  6. 2021Kosp2e: Korean Speech to English Translation Corpuscited by 7
  7. 2022Bangla-Wave: Improving Bangla Automatic Speech Recognition Utilizing N-gram Language Modelscited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 1.

4 papers.

  1. 2025TituLLMs: A Family of Bangla LLMs with Comprehensive Benchmarkingcited by 9
  2. 2024Empowering Low-Resource Language ASR via Large-Scale Pseudo Labelingcited by 5
  3. 2026Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignmentcited by 1
  4. 2025BnTTS: Few-Shot Speaker Adaptation in Low-Resource Settingcited by 0