解决西班牙语转录歧义：用于标点恢复的混合声学-词汇系统
Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical System for Punctuation Restoration
AI总结：
针对西班牙语无标点陈述与疑问句的歧义，提出混合声学-词汇标点恢复系统，整合声学与词汇信号，提升问号F1及整体标点恢复，优于LLM并降低ASR词错误率。
摘要：
标点恢复是自动语音识别（ASR）系统之后的关键步骤，旨在提高转录文本的可读性并促进后续自然语言处理任务。然而，传统的基于词汇的方法不足以解决西班牙语中的标点恢复任务，因为西班牙语中常出现无标点陈述句与疑问句之间的歧义。在本研究中，我们提出了一种新颖的混合声学-词汇标点恢复系统，用于西班牙语转录，通过模块化过程整合声学与词汇信号。实验结果表明，所提出的系统能够有效提升问号F1分数及整体标点恢复性能，在公开和内部西班牙语会话数据集上均表现优异。此外，与大型语言模型（LLM）的基准对比表明，我们的方法在准确性、可靠性和延迟方面具有优越性。进一步地，我们证明了ASR模块的词错误率（WER）也受益于所提出的系统。
英文摘要：
Punctuation restoration is a crucial step after Automatic Speech Recognition (ASR) systems to enhance transcript readability and facilitate subsequent NLP tasks. Nevertheless, conventional lexical-based approaches are inadequate for solving the punctuation restoration task in Spanish, where ambiguity can be often found between unpunctuated declaratives and questions. In this study, we propose a novel hybrid acoustic-lexical punctuation restoration system for Spanish transcription, which consolidates acoustic and lexical signals through a modular process. Our experiment results show that the proposed system can effectively improve F1 score of question marks and overall punctuation restoration on both public and internal Spanish conversational datasets. Additionally, benchmark comparison against LLMs (Large Language Model) indicates the superiority of our approach in accuracy, reliability and latency. Furthermore, we demonstrate that the Word Error Rate (WER) of the ASR module also benefits from our proposed system.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
