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arXiv · 2205.13961

Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning

Xiliang Zhu·Shayna Gardiner·David Rossouw·Tere Roldán·Simon Corston-Oliver
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Abstract

Automatic Speech Recognition (ASR) systems typically produce unpunctuated transcripts that have poor readability. In addition, building a punctuation restoration system is challenging for low-resource languages, especially for domain-specific applications. In this paper, we propose a Spanish punctuation restoration system designed for a real-time customer support transcription service. To address the data sparsity of Spanish transcripts in the customer support domain, we introduce two transfer-learning-based strategies: 1) domain adaptation using out-of-domain Spanish text data; 2) cross-lingual transfer learning leveraging in-domain English transcript data. Our experiment results show that these strategies improve the accuracy of the Spanish punctuation restoration system.

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Xiliang Zhu, Shayna Gardiner, David Rossouw, Tere Roldán, Simon Corston-Oliver. 2022-05-27. Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning. https://arxiv.org/abs/2205.13961

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