Diffusion Language Models for Speech Recognition
Diffusion language models have recently emerged as a leading alternative to standard language models, due to their ability for bidirectional attention and parallel text generation. In this work, we explore variants for their use in speech recognition. Specifically, we introduce a comprehensive guide to incorporating masked diffusion language models (MDLM) and uniform-state diffusion models (USDMs) for rescoring ASR hypotheses. Additionally, we design a new joint-decoding method that combines CTC and USDM by integrating the framewise probability distributions derived from CTC with the labelwise probability distributions computed by USDM at each decoding step, thereby generating new candida...
| Authors | Davyd Naveriani, Albert Zeyer, Ralf Schlüter et al. |
|---|---|
| Primary category | cs.CL |
| Published | 15 April 2026 |
| Last updated at source | 29 April 2026 |
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