研究人员开发了一种新的方法来改进自动语音识别（ASR）系统，通过整合持续学习技术来更好地处理语音不流畅。该方法包括将明确的不流畅标记引入预训练的ASR模型，然后在多样化的数据集上进行微调。此过程旨在防止通用知识的灾难性遗忘，同时增强模型识别和处理语音不流畅的能力，从而解决当前ASR技术的一个关键挑战。 AI
影响 这项研究可能带来更强大的ASR系统，能够处理自然、非脚本化的语音，改善语音启用应用程序的用户体验。
排序理由 该集群包含一篇详细介绍ASR新研究方法的学术论文。
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arXiv:2606.14391v1 Announce Type: cross Abstract: Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, leading to information loss and hallucinations. Prior …
Despite advances in large-scale Automatic Speech Recognition (ASR), disfluent speech remains challenging, as state-of-the-art systems are often optimized to omit disfluencies, leading to information loss and hallucinations. Prior work has focused on verbatim transcription and the…