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Improving Data Driven Inverse Text Normalization using Data Augmentation. (arXiv:2207.09674v1 [cs.CL])
July 21, 2022, 1:10 a.m. | Laxmi Pandey, Debjyoti Paul, Pooja Chitkara, Yutong Pang, Xuedong Zhang, Kjell Schubert, Mark Chou, Shu Liu, Yatharth Saraf
cs.LG updates on arXiv.org arxiv.org
Inverse text normalization (ITN) is used to convert the spoken form output of
an automatic speech recognition (ASR) system to a written form. Traditional
handcrafted ITN rules can be complex to transcribe and maintain. Meanwhile
neural modeling approaches require quality large-scale spoken-written pair
examples in the same or similar domain as the ASR system (in-domain data), to
train. Both these approaches require costly and complex annotations. In this
paper, we present a data augmentation technique that effectively generates rich
spoken-written …
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