新的纯文本方法可适应语音识别模型

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-26 04:00

研究人员开发了 WhisTLE，一种仅使用文本数据来适应预训练自动语音识别 (ASR) 模型的新颖方法。该技术采用变分自编码器对编码器输出进行建模，并微调解码器，可选择性地整合文本到语音合成。WhisTLE 显著降低了词错误率，在大多数测试场景中均优于其他适应方法，且不增加运行时成本。 AI

影响提供了一种更有效的方法，仅使用文本即可将 ASR 模型适应特定领域，有可能提高专业应用的准确性。

排序理由关于 ASR 模型域适应新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CL TIER_1 English(EN) · Akshat Pandey, Karun Kumar, Raphael Tang · 2026-05-26 04:00

WhisTLE：预训练语音识别 Transformer 的深度监督、纯文本域自适应

arXiv:2509.10452v2 Announce Type: replace Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance. In many real-world settings, collecting speech data is impractical, necessitating text…