Drax: Speech Recognition with Discrete Flow Matching
Authors: name: Aviv Navon, name: Aviv Shamsian, name: Neta Glazer, name: Yael Segal-Feldman, name: Gill Hetz, name: Joseph Keshet, name: Ethan Fetaya
Organization: aiOla

Diffusion and flow-based non-autoregressive (NAR) models have shown strong
promise in large language modeling, however, their potential for automatic
speech recognition (ASR) remains largely unexplored. We propose Drax, a
discrete flow matching framework for ASR that enables efficient parallel
decoding. To better align training with inference, we construct an
audio-conditioned probability path that guides the model through trajectories
resembling likely intermediate inference errors, rather than direct random
noise to target transitions. Our theoretical analysis links the generalization
gap to divergences between training and inference occupancies, controlled by
cumulative velocity errors, thereby motivating our design choice. Empirical
evaluation demonstrates that our approach attains recognition accuracy on par
with state-of-the-art speech models while offering improved accuracy-efficiency
trade-offs, highlighting discrete flow matching as a promising direction for
advancing NAR ASR.