Improving Data Driven Inverse Text Normalization using Data Augmentation

TL;DR

This paper introduces a data augmentation method for inverse text normalization that generates spoken-written numeric pairs from out-of-domain data, improving model accuracy without extensive annotations.

Contribution

The paper presents a novel data augmentation technique for ITN that reduces annotation costs and enhances model performance across various numeric surfaces.

Findings

Achieved 14.44% higher accuracy with augmented data

Outperforms models trained only on in-domain data

Effective for multiple numeric surface types

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%.