Drax: Speech Recognition with Discrete Flow Matching
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.
68e55fb1975ac4c405ef1f2f
Aviv Navon
68e55fb1975ac4c405ef1f30
Aviv Shamsian
68e55fb1975ac4c405ef1f31
Neta Glazer
68e55fb1975ac4c405ef1f32
Yael Segal-Feldman
68e55fb1975ac4c405ef1f33
Gill Hetz
68e55fb1975ac4c405ef1f34
Joseph Keshet
68e55fb1975ac4c405ef1f35
Ethan Fetaya
Drax: Speech Recognition with Discrete Flow Matching
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.
Drax, a discrete flow matching framework for ASR, achieves state-of-the-art recognition accuracy with improved efficiency by constructing an audio-conditioned probability path.
diffusion models flow-based models non-autoregressive models discrete flow matching audio-conditioned probability path generalization gap divergences cumulative velocity errors ASR recognition accuracy accuracy-efficiency trade-offs