Decoding Strategies for Diffusion-Based ASR: A Systematic Evaluation of Confidence-Based Thresholding
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| Main Authors: | , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917543298465792 |
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| author | Yeo, Jeong Hun Kim, Minsu Rha, Hyeongseop Ro, Yong Man |
| author_facet | Yeo, Jeong Hun Kim, Minsu Rha, Hyeongseop Ro, Yong Man |
| contents | While LLM-based Automatic Speech Recognition (ASR) achieves high accuracy, its speed is limited by sequential autoregressive decoding. Diffusion Language Models (DLMs) offer a parallel alternative, yet their decoding strategies remain under-explored in ASR contexts. This paper analyzes three decoding schemes for DLM-based ASR: fixed-number, static confidence threshold, and dynamic confidence threshold. We propose measuring round-wise accuracy using Negative Log-Likelihood-based uncertainty as a proxy for decoding progress. Our results show that both threshold-based strategies significantly outperform fixed-number schemes in accuracy and speed. We attribute this to a property unique to ASR: most tokens reach high confidence early, allowing reliable ones to be harvested aggressively while leaving only difficult tokens for later rounds. Notably, the static-threshold strategy matches the accuracy of autoregressive decoding while offering superior efficiency. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2605_29613 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Decoding Strategies for Diffusion-Based ASR: A Systematic Evaluation of Confidence-Based Thresholding Yeo, Jeong Hun Kim, Minsu Rha, Hyeongseop Ro, Yong Man Audio and Speech Processing Sound While LLM-based Automatic Speech Recognition (ASR) achieves high accuracy, its speed is limited by sequential autoregressive decoding. Diffusion Language Models (DLMs) offer a parallel alternative, yet their decoding strategies remain under-explored in ASR contexts. This paper analyzes three decoding schemes for DLM-based ASR: fixed-number, static confidence threshold, and dynamic confidence threshold. We propose measuring round-wise accuracy using Negative Log-Likelihood-based uncertainty as a proxy for decoding progress. Our results show that both threshold-based strategies significantly outperform fixed-number schemes in accuracy and speed. We attribute this to a property unique to ASR: most tokens reach high confidence early, allowing reliable ones to be harvested aggressively while leaving only difficult tokens for later rounds. Notably, the static-threshold strategy matches the accuracy of autoregressive decoding while offering superior efficiency. |
| title | Decoding Strategies for Diffusion-Based ASR: A Systematic Evaluation of Confidence-Based Thresholding |
| topic | Audio and Speech Processing Sound |
| url | https://arxiv.org/abs/2605.29613 |