# Automatic Heteronym Resolution Pipeline Using RAD-TTS Aligners

## TL;DR

This paper introduces an automated pipeline using RAD-TTS aligners to disambiguate heteronyms in G2P datasets, reducing reliance on manual labeling and improving pronunciation accuracy for TTS systems.

## Contribution

The paper presents a novel RAD-TTS aligner-based method for automatic heteronym disambiguation in G2P datasets, enhancing dataset quality without extensive manual effort.

## Key findings

- Effective disambiguation of heteronyms in datasets
- Improved G2P training data quality
- Potential for reducing manual annotation costs

## Abstract

Grapheme-to-phoneme (G2P) transduction is part of the standard text-to-speech (TTS) pipeline. However, G2P conversion is difficult for languages that contain heteronyms -- words that have one spelling but can be pronounced in multiple ways. G2P datasets with annotated heteronyms are limited in size and expensive to create, as human labeling remains the primary method for heteronym disambiguation. We propose a RAD-TTS Aligner-based pipeline to automatically disambiguate heteronyms in datasets that contain both audio with text transcripts. The best pronunciation can be chosen by generating all possible candidates for each heteronym and scoring them with an Aligner model. The resulting labels can be used to create training datasets for use in both multi-stage and end-to-end G2P systems.