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English(EN) Dual-Form ASR: Semantics-Aware Inverse Text Normalization for Chinese Speech Recognition

新的DF-ASR框架将逆文本规范化集成到语音识别中

研究人员开发了双形式自动语音识别(DF-ASR),这是一个将逆文本规范化(ITN)直接集成到自动语音识别(ASR)过程中的新颖框架。与传统的级联系统不同,DF-ASR使用成对的口语形式和书面形式监督,并通过LLM驱动的工作流程和称为ITN-MWER的序列级目标进行增强。通过将规范化与声学-上下文建模一起考虑,该方法旨在提高转录的准确性和可读性,特别是对于语义依赖的数字表达式。在中国语音数据上进行的实验表明,DF-ASR的性能优于现有的开源ASR-ITN系统,并且与闭源参考系统相比仍具有竞争力,同时还提供了对转录形式的可靠控制。 AI

影响 这项研究可能带来更准确、更易读的语音转文本系统,特别是对于复杂的数字表达式。

排序理由 该集群包含一篇详细介绍语音识别新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新的DF-ASR框架将逆文本规范化集成到语音识别中

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该集群包含一篇详细介绍语音识别新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Fengrun Zhang, Li Fu, Wangjin Zhou, Lu Fan, Youzheng Wu, Xiaodong He ·

    双模态ASR:面向中文语音识别的语义感知逆文本规范化

    arXiv:2609.02901v1 Announce Type: new Abstract: Modern automatic speech recognition (ASR) scenarios require both spoken-form transcripts for faithful transcription and readable written-form transcripts with inverse text normalization (ITN). However, these forms are typically prod…