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English(EN) Personalized Automatic Speech Recognition for a Dysarthric and Tracheostomic Speaker using Artificial Conversations

个性化自动语音识别系统为构音障碍患者的错误率降低了50%

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员为一名患有构音障碍和气管切开的捷克语者开发了一个个性化的自动语音识别（ASR）系统，该患者的语音否则无法理解。该系统采用多阶段训练流程，在包括通过“人工智能对话”协议收集的患者自身语音在内的各种数据集上微调了Whisper Base模型。与基线Whisper Base相比，该方法将字符错误率（CER）相对降低了50%，证明了为严重言语障碍者创建有用的ASR系统的可行性。 AI

影响 展示了提高严重言语障碍者ASR可访问性的潜力。

排序理由 该集群包含一篇详细介绍新研究方法和结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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个性化自动语音识别系统为构音障碍患者的错误率降低了50%

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该集群包含一篇详细介绍新研究方法和结果的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · David Nadrchal, Monorama Swain, Florian Schmid, Gerhard Widmer, Paul Primus ·

    面向构音障碍和气管切开患者的个性化自动语音识别：基于人工智能对话

    arXiv:2610.03017v1 Announce Type: new Abstract: This work presents an automatic speech recognition (ASR) system personalized for a Czech speaker with a permanent tracheal stoma and severe dysarthria rendering their speech unintelligible to untrained listeners. We release a public…

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