  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.
