# WST: Weakly Supervised Transducer for Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2511.04035 · HTML: https://mlsanity.com/paper/2511.04035/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 22 references that are in the map.
- First posted on arXiv 6 Nov 2025.

## Builds on (most cited first, 15 of 15)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [Listen, Attend and Spell](https://mlsanity.com/paper/1508.01211/) (2015)
- [Scaling Speech Technology to 1,000+ Languages](https://mlsanity.com/paper/2305.13516/) (2023)
- [Recent Advances in End-to-End Automatic Speech Recognition](https://mlsanity.com/paper/2111.01690/) (2021)
- [On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition](https://mlsanity.com/paper/2005.14327/) (2020)
- [Reproducing Whisper-Style Training Using an Open-Source Toolkit and Publicly Available Data](https://mlsanity.com/paper/2309.13876/) (2023)
- [Exploring Transformers for Large-Scale Speech Recognition](https://mlsanity.com/paper/2005.09684/) (2020)
- [Transformer in action: a comparative study of transformer-based acoustic models for large scale speech recognition applications](https://mlsanity.com/paper/2010.14665/) (2020)
- [Star Temporal Classification: Sequence Classification with Partially Labeled Data](https://mlsanity.com/paper/2201.12208/) (2022)
- [Bypass Temporal Classification: Weakly Supervised Automatic Speech Recognition with Imperfect Transcripts](https://mlsanity.com/paper/2306.01031/) (2023)
- [Learning from Flawed Data: Weakly Supervised Automatic Speech Recognition](https://mlsanity.com/paper/2309.15796/) (2023)
- [Powerful and Extensible WFST Framework for RNN-Transducer Losses](https://mlsanity.com/paper/2303.10384/) (2023)
- [Token-Weighted RNN-T for Learning from Flawed Data](https://mlsanity.com/paper/2406.18108/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Training Criterion with Token-Level Tolerance to Transcription Ambiguity for Automatic Speech Recognition](https://mlsanity.com/paper/2609.30160/) (2026)

## Abstract (arXiv)

The Recurrent Neural Network-Transducer (RNN-T) is widely adopted in end-to-end (E2E) automatic speech recognition (ASR) tasks but depends heavily on large-scale, high-quality annotated data, which are often costly and difficult to obtain. To mitigate this reliance, we propose a Weakly Supervised Transducer (WST), which integrates a flexible training graph designed to robustly handle errors in the transcripts without requiring additional confidence estimation or auxiliary pre-trained models. Empirical evaluations on synthetic and industrial datasets reveal that WST effectively maintains performance even with transcription error rates of up to 70%, consistently outperforming existing Connectionist Temporal Classification (CTC)-based weakly supervised approaches, such as Bypass Temporal Classification (BTC) and Omni-Temporal Classification (OTC). These results demonstrate the practical utility and robustness of WST in realistic ASR settings. The implementation will be publicly available.