  Diffusion models have shown exceptional scaling properties in the image
synthesis domain, and initial attempts have shown similar benefits for applying
diffusion to unconditional text synthesis. Denoising diffusion models attempt
to iteratively refine a sampled noise signal until it resembles a coherent
signal (such as an image or written sentence). In this work we aim to see
whether the benefits of diffusion models can also be realized for speech
recognition. To this end, we propose a new way to perform speech recognition
using a diffusion model conditioned on pretrained speech features.
Specifically, we propose TransFusion: a transcribing diffusion model which
iteratively denoises a random character sequence into coherent text
corresponding to the transcript of a conditioning utterance. We demonstrate
comparable performance to existing high-performing contrastive models on the
LibriSpeech speech recognition benchmark. To the best of our knowledge, we are
the first to apply denoising diffusion to speech recognition. We also propose
new techniques for effectively sampling and decoding multinomial diffusion
models. These are required because traditional methods of sampling from
acoustic models are not possible with our new discrete diffusion approach. Code
and trained models are available: https://github.com/RF5/transfusion-asr
