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 candidates that combine strong language knowledge from USDM and acoustic information from CTC. Our findings reveal that USDM, as well as MDLM, can significantly improve the accuracy of recognized text. We publish all our code and recipes.
Figures & tables
Figure 1: Overview of the proposed joint CTC-USDM decoding. At each denoising step, the USDM token-level distribution is combined with the CTC frame-level distribution to sample input to the next denoising step.
Figure 2: Dev PPL learning curves for MDLM and USDM trained for 10 full epochs (1 full epoch = 20 subepochs).
Figure 3: WER [%] on dev-other comparing MDLM rescoring (sequence-level, global-mask and sample-mask score normalization), MDLM (5 ep) coupled scoring, and USDM (5 ep) rescoring, across different numbers of Monte Carlo samples ( K ). Stars mark the best WER for each method.