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arXiv · 2004.05985

Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?

Łukasz Augustyniak·Piotr Szymanski·Mikołaj Morzy·Piotr Zelasko·Adrian Szymczak·Jan Mizgajski·Yishay Carmiel·Najim Dehak
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Abstract

Automatic Speech Recognition (ASR) systems introduce word errors, which often confuse punctuation prediction models, turning punctuation restoration into a challenging task. These errors usually take the form of homonyms. We show how retrofitting of the word embeddings on the domain-specific data can mitigate ASR errors. Our main contribution is a method for better alignment of homonym embeddings and the validation of the presented method on the punctuation prediction task. We record the absolute improvement in punctuation prediction accuracy between 6.2% (for question marks) to 9% (for periods) when compared with the state-of-the-art model.

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Łukasz Augustyniak, Piotr Szymanski, Mikołaj Morzy, Piotr Zelasko, Adrian Szymczak, Jan Mizgajski, Yishay Carmiel, Najim Dehak. 2020-04-13. Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?. https://arxiv.org/abs/2004.05985

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