Non-Autoregressive Minimum Bayes' Risk Decoding for Fast Speech Recognition
Non-autoregressive (NAR) decoding generates output tokens in parallel, making speech recognition faster than autoregressive decoding, which generates them sequentially from left to right. However, the recognition performance is degraded because NAR decoding cannot resolve uncertainty by conditioning on previously generated tokens. To address this issue, we propose a novel NAR decoding framework based on minimum Bayes' risk (MBR) decoding, termed NAR-MBR decoding, that maximizes the expected utility calculated from samples drawn from the output probability of an NAR model rather than maximizing the output probability. Notably, by leveraging the nature of NAR models, multiple samples are ob...
| Authors | Hiroyuki Deguchi, Takatomo Kano, Katsuki Chousa et al. |
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| Primary category | eess.AS |
| Published | 16 June 2026 |
| Last updated at source | 16 June 2026 |
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