Abstract
Recent work has shown that sample-based Minimum Bayes Risk (MBR) decoding outperforms beam search in text-to-text generation tasks, such as machine translation, text summarization, and image captioning. On the other hand, beam search is the current practice for speech-to-text tasks such as automatic speech recognition (ASR) and Speech Translation (ST). Given that MBR decoding is effective in text-to-text generation tasks, it is reasonable to expect it to also be effective for speech-to-text tasks. In this paper, we evaluate MBR decoding for ASR and ST tasks on English and Japanese using Whisper and its derivative models. We observe that the accuracy of MBR decoding outperforms that of beam search in most of the experimental settings we have evaluated. The results show that MBR decoding is a promising method for offline ASR and ST tasks that require high accuracy. The code is available at this https URL
Abstract (translated)
最近的研究表明，在文本到文本生成任务（如机器翻译、文本摘要和图像描述）中，基于样本的最小贝叶斯风险(Minimum Bayes Risk, MBR)解码优于束搜索。然而，在语音转文本的任务（例如自动语音识别(ASR) 和语音翻译(ST)）中，当前实践中普遍使用束搜索方法。鉴于MBR 解码在文本到文本生成任务中的有效性，可以合理地期望它同样适用于语音转文本的任务。在这篇论文中，我们评估了MBR解码在英语和日语的自动语音识别（ASR）以及语音翻译(ST)任务上的表现，使用的是Whisper及其衍生模型。我们观察到，在大多数实验设置下，MBR 解码的准确性优于束搜索方法。研究结果表明，对于需要高准确性的离线 ASR 和 ST 任务来说，MBR 解码是一种有前景的方法。相关代码可以在[这里](https://URL)获取。
URL
https://arxiv.org/abs/2510.19471
