Resolving Transcription Ambiguity in Spanish: A Hybrid Acoustic-Lexical
  System for Punctuation Restoration
Xiliang Zhu Chia-Tien Chang Shayna Gardiner David Rossouw Jonas Robertson
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.