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English(EN) Reinforcement Learning for Data-Efficient Code-Switched ASR

新的强化学习方法提高了语码转换语音识别的数据效率

研究人员开发了一种新颖的强化学习技术 RLVR,以提高语码转换自动语音识别 (ASR) 的音频语言模型的数据效率。该方法利用基于策略的组相对策略优化,并结合错误率和脚本保真度奖励来适应 Qwen2-Audio 等模型。实验表明,仅使用 10% 数据训练的 RLVR,其性能可以媲美使用完整数据集的监督微调,尤其在类型学上距离较远的语言对上表现出色,并将收益转移到人工录制的语音上。 AI

影响 这项研究可以显著降低训练语码转换 ASR 系统的所需数据量,使其更易于访问和更高效。

排序理由 详细介绍 ASR 新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新的强化学习方法提高了语码转换语音识别的数据效率

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详细介绍 ASR 新方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Ziwei Ye, Peter Vickers ·

    用于数据高效语码转换语音识别的强化学习

    arXiv:2607.02757v1 Announce Type: new Abstract: Audio-language models can be prompted for code-switched speech, but their decoding is not optimized for code-switching and often fails at language boundaries. We propose a practical reinforcement learning with verifiable rewards rec…