重新评估最小贝叶斯风险解码在自动语音识别中的应用
Re-evaluating Minimum Bayes Risk Decoding for Automatic Speech Recognition
AI总结：
本文评估了最小贝叶斯风险解码在英语和日语自动语音识别和语音翻译任务中的性能，发现其在多数实验设置中优于束搜索，显示出在高精度任务中的潜力。
摘要：
近期研究表明，基于采样的最小贝叶斯风险（MBR）解码在文本到文本生成任务中优于束搜索，如机器翻译、文本摘要和图像描述。然而，束搜索仍是自动语音识别（ASR）和语音翻译（ST）任务中的常用方法。鉴于MBR解码在文本到文本生成任务中有效，本文评估了其在ASR和ST任务中的表现，使用Whisper及其衍生模型进行实验。结果显示，MBR解码在多数实验设置中优于束搜索，表明其在需要高精度的离线ASR和ST任务中具有潜力。代码可在https://github.com/CyberAgentAILab/mbr-for-asr获取。
英文摘要：
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 https://github.com/CyberAgentAILab/mbr-for-asr
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
