A Two-Step Approach for Data-Efficient French Pronunciation Learning

TL;DR

This paper introduces a two-step method for French pronunciation learning that efficiently utilizes limited data, combining grapheme-to-phoneme conversion and post-lexical processing to address complex phonological phenomena.

Contribution

It presents a novel two-step approach that reduces dependency on extensive labeled data for French pronunciation modeling, suitable for resource-constrained settings.

Findings

Effective in low-resource environments

Mitigates data scarcity issues

Improves French phonological modeling

Abstract

Recent studies have addressed intricate phonological phenomena in French, relying on either extensive linguistic knowledge or a significant amount of sentence-level pronunciation data. However, creating such resources is expensive and non-trivial. To this end, we propose a novel two-step approach that encompasses two pronunciation tasks: grapheme-to-phoneme and post-lexical processing. We then investigate the efficacy of the proposed approach with a notably limited amount of sentence-level pronunciation data. Our findings demonstrate that the proposed two-step approach effectively mitigates the lack of extensive labeled data, and serves as a feasible solution for addressing French phonological phenomena even under resource-constrained environments.