Improving Data Driven Inverse Text Normalization using Data Augmentation

• URL: http://arxiv.org/abs/2207.09674v1
• Date: Wed, 20 Jul 2022 06:07:26 GMT
• Title: Improving Data Driven Inverse Text Normalization using Data Augmentation
• Authors: Laxmi Pandey, Debjyoti Paul, Pooja Chitkara, Yutong Pang, Xuedong Zhang, Kjell Schubert, Mark Chou, Shu Liu, Yatharth Saraf
• Abstract summary: Inverse text normalization (ITN) is used to convert the spoken form output of an automatic speech recognition (ASR) system to a written form. We present a data augmentation technique that effectively generates rich spoken-written numeric pairs from out-of-domain textual data. We empirically demonstrate that ITN model trained using our data augmentation technique consistently outperform ITN model trained using only in-domain data.
• Score: 14.820077884045645
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 numeric pairs from out-of-domain textual data with minimal human annotation. We empirically demonstrate that ITN model trained using our data augmentation technique consistently outperform ITN model trained using only in-domain data across all numeric surfaces like cardinal, currency, and fraction, by an overall accuracy of 14.44%.

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