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arXiv · 2511.04035

WST: Weakly Supervised Transducer for Automatic Speech Recognition

Dongji Gao·Chenda Liao·Changliang Liu·Matthew Wiesner·Leibny Paola Garcia·Daniel Povey·Sanjeev Khudanpur·Jian Wu
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Abstract

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.

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Dongji Gao, Chenda Liao, Changliang Liu, Matthew Wiesner, Leibny Paola Garcia, Daniel Povey, Sanjeev Khudanpur, Jian Wu. 2025-11-06. WST: Weakly Supervised Transducer for Automatic Speech Recognition. https://arxiv.org/abs/2511.04035

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