# MDM-ASR: Bridging Accuracy and Efficiency in ASR with Diffusion-Based Non-Autoregressive Decoding

arXiv: https://arxiv.org/abs/2602.18952 · HTML: https://mlsanity.com/paper/2602.18952/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2026.

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

- [Full-duplex Spoken Language Modeling with Discrete Diffusion](https://mlsanity.com/paper/2610.17780/) (2026)

## Abstract (arXiv)

In sequence-to-sequence Transformer ASR, autoregressive (AR) models achieve strong accuracy but suffer from slow decoding, while non-autoregressive (NAR) models enable parallel decoding at the cost of degraded performance. We propose a principled NAR ASR framework based on Masked Diffusion Models to reduce this gap. A pre-trained speech encoder is coupled with a Transformer diffusion decoder conditioned on acoustic features and partially masked transcripts for parallel token prediction. To mitigate the training-inference mismatch, we introduce Iterative Self-Correction Training that exposes the model to its own intermediate predictions. We also design a Position-Biased Entropy-Bounded Confidence-based sampler with positional bias to further boost results. Experiments across multiple benchmarks demonstrate consistent gains over prior NAR models and competitive performance with strong AR baselines, while retaining parallel decoding efficiency.