BigSSL: Exploring the Frontier of Large-Scale Semi-Supervised Learning for Automatic Speech Recognition
Open accessAbstract
We summarize the results of a host of efforts using giant automatic speech recognition (ASR) models pre-trained using large, diverse unlabeled datasets containing approximately a million hours of audio. We find that the combination of pre-training, self-training and scaling up model size greatly increases data efficiency, even for extremely large tasks with tens of thousands of hours of labeled data. In particular, on an ASR task with 34 k hours of labeled data, by fine-tuning an 8 billion parameter pre-trained Conformer model we can match state-of-the-art (SoTA) performance with only 3% of the training data and significantly improve SoTA with the full training set. We also report on the universal benefits gained from using big pre-trained and self-trained models for a large set of downstream tasks that cover a wide range of speech domains and span multiple orders of magnitudes of dataset sizes, including obtaining SoTA performance on many public benchmarks. In addition, we utilize the learned representation of pre-trained networks to achieve SoTA results on non-ASR tasks.
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Some ASR tests produce error rates lower than manual transcription.
“Some of these tests produce very low error rates that even outperform those achieved through manual transcription [Zhang et al. 2022].”
From Measuring the Accuracy of Automatic Speech Recognition Solutions (2023) · cites this paper as Zhang et al. 2022
Table III in “English (US) Voice Search” (p. 6) reports a 4.1% word error rate, supporting the claim that some ASR tests achieve low error rates. Neither that table nor the passages found by searching for manual transcription and human performance provide a human-transcription error rate, so the claimed comparison is unsupported. (LLM, conf=0.83)
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Self-supervised random forests for robust voice activity detection with limited labeled data
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Tompkins, D. G., Kumar, K., & Wu, J. (2022) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp43922.2022.9747250 · Cited by 4 · see citing works
More Speaking or More Speakers?
Berrebbi, D., Collobert, R., Jaitly, N., & Likhomanenko, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096078 · Cited by 4 · see citing works
Foundation Models for Speech, Images, Videos, and Control
Paaß, G., & Giesselbach, S. (2023) · Artificial intelligence: foundations, theory, and algorithms/Artificial intelligence: Foundations, theory, and algorithms · doi:10.1007/978-3-031-23190-2_7 · Cited by 5 · see citing works
RAttSR: A Novel Low-Cost Reconstructed Attention-Based End-to-End Speech Recognizer
Paul, B., & Phadikar, S. (2023) · Circuits Systems and Signal Processing · doi:10.1007/s00034-023-02570-5 · Cited by 4 · see citing works
Bittar, A., Dixon, P. R., Samragh, M., Nishu, K., & Naik, D. K. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447485 · Cited by 4 · see citing works
Weighted Ensemble Learning for Accident Severity Classification Using Social Media Data
Raul, S. K., Rout, R. R., & Somayajulu, D. V. L. N. (2024) · SN Computer Science · doi:10.1007/s42979-024-02870-w · Cited by 4 · see citing works
Henkel, O., Horne-Robinson, H., Hills, L., Roberts, B., & McGrane, J. A. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00435-9 · Cited by 5 · see citing works
Resource-Efficient Transfer Learning from Speech Foundation Model Using Hierarchical Feature Fusion
Huo, Z., Sim, K. C., Li, B., Hwang, D., Sainath, T. N., & Strohman, T. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096799 · Cited by 4 · see citing works
Self-citationKMSAV: Korean multi‐speaker spontaneous audiovisual dataset
Park, K., Oh, C., & Dong, S. (2024) · ETRI Journal · doi:10.4218/etrij.2023-0352 · Cited by 4 · see citing works
Multilingual Speech Recognition using Knowledge Transfer across Learning ProcessesPreprint
Lahiri, R., Kumatani, K., Sun, E., & Qian, Y. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07909 · Cited by 4 · see citing works
Improving Audio Classification Method by Combining Self-Supervision with Knowledge Distillation
Gong, X., Duan, H., Yang, Y., Tan, L., Wang, J., & Vasilakos, A. V. (2023) · Electronics · doi:10.3390/electronics13010052 · Cited by 3 · see citing works
Jing, W., Lu, L., Liu, Q., & Chen, Z. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130005 · Cited by 3 · see citing works
Self-citationKnowledge Management Framework Over Low Resource Indian Colloquial Language Audio Contents
Srivatsa, S., Aparna, M., Madhavan, G. S., & Srinivasa, S. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3632410.3632483 · Cited by 3 · see citing works
Multi-Head Attention-Enhanced Speech Recognition for Reduced Data Requirements
Li, Y., Zhou, Y., Qiu, Z., Wang, Y., Wang, J., & Huang, G. (2024) · Electronics · doi:10.3390/electronics14010128 · Cited by 4 · see citing works
Self-citationToken-Weighted RNN-T For Learning From Flawed Data
Keren, G., Zhou, W. M., & Kalinli, O. (2024) · IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt61566.2024.10832209 · Cited by 3 · see citing works
Li, Z., Xia, T., Chang, Y., & Wu, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130711 · Cited by 3 · see citing works
Self-citationCross-Training: A Semi-Supervised Training Scheme for Speech Recognition
Khorram, S., Tripathi, A., Kim, J., Han, L., Zhang, Q., Prabhavalkar, R., & Sak, H. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096978 · Cited by 3 · see citing works
Novel Rifle Number Recognition Based on Improved YOLO in Military Environment
Kwon, H., & Lee, S. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.042466 · Cited by 3 · see citing works
Task Vector Algebra for ASR Models
Ramesh, G., Audhkhasi, K., & Ramabhadran, B. (2024) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp48485.2024.10447848 · Cited by 3 · see citing works
Self-citationDeep learning-based Soft word embedding approach for sentiment analysis
Ouni, C., Benmohamed, E., & Ltifi, H. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.09.720 · Cited by 3 · see citing works
Xu, C., Liu, X., Zhang, Y., Ma, A., Xiao, T., Zhu, J., Man, D., & Yang, W. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3555070 · Cited by 3 · see citing works
Self-citationSpeech Feature Extraction in Broadcast Hosting Based on Fluctuating Equation Inversion
Xu, C. (2024) · Journal of Advanced Computational Intelligence and Intelligent Informatics · doi:10.20965/jaciii.2024.p0762 · Cited by 2 · see citing works
Boosting grapevine phenological stages prediction based on climatic data by pseudo-labeling approach
Fasihi, M., Sodini, M., Falcon, A., Degano, F., Sivilotti, P., & Serra, G. (2025) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2025.03.003 · Cited by 2 · see citing works
Kermanshahi, M. A., Akbari, A., Nasersharif, B., & Kabudian, S. J. (2025) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2025.110851 · Cited by 2 · see citing works
Revealing the Role of Audio Channels in ASR Performance Degradation
Huang, K., Chen, L.-W., Lee, H.-S., Chen, B., & Wang, H.-M. (2025) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru65441.2025.11434673 · Cited by 2 · see citing works
Thimmaraja Yadava, G., Nagaraja, B. G., Jayanna, H. S., & Shivakumar, B. R. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16554-9 · Cited by 2 · see citing works
UniEnc-CASSNAT: An Encoder-Only Non-Autoregressive ASR for Speech SSL Models
Fan, R., Shankar, N. B., & Alwan, A. A. (2024) · IEEE Signal Processing Letters · doi:10.1109/lsp.2024.3365036 · Cited by 2 · see citing works
Retracted: Exploring Reinforcement Learning in Large-Scale Data Processing
Bhambu, P., Kumar, R., Sharmila, P., Patil, V. D., Khurana, S., & Vivek, V. (2023) · International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) · doi:10.1109/smartgencon60755.2023.10442194 · Cited by 3 · see citing works
Retracted
- Authors
- Zhang, Y., Park, D. S., Han, W., Qin, J., Gulati, A., Shor, J., Jansen, A., Xu, Y., Huang, Y., Wang, S., Zhou, Z., Li, B., Ma, M., Chan, W., Yu, J., Wang, Y., Cao, L., Sim, K. C., Ramabhadran, B., Sainath, T. N., Beaufays, F., Chen, Z., Le, Q. V., Chiu, C.-C., Pang, R., & Wu, Y.
- Affiliations
- Google (United States) (US)
- Google, Inc., USA
- ORCID
- Venue
- IEEE Journal of Selected Topics in Signal Processing
- Published
- 2022-10
- Volume
- 16
- Issue
- 6
- Pages
- 1519-1532
- ISSN
- 1932-4553 (ISSN-L)
- 1941-0484
- OpenAlex
- W3204696009
- Cited by
- 161
- References
- 114
- Citations by year
- 2026: 11 · 2025: 28 · 2024: 40 · 2023: 51 · 2022: 28 · 2021: 3
- FWCI
- 12.09
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Journal of Selected Topics in Signal Processing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- speech recognition, pre-training, self-training, data efficiency, conformational isomers, model scaling, transfer learning, representation learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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