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A Comprehensive Analysis of Tokenization and Self-Supervised Learning in End-to-End Automatic Speech Recognition applied on French Language

2026-05-05 12:39:40
Thibault Ba\~neras-Roux, Mickael Rouvier, Jane Wottawa, Richard Dufour
arXiv_CL
         
Abstract

The performance of end-to-end automatic speech recognition (ASR) systems enables their increasing integration into numerous applications. While there are various benefits to such speech-to-text systems, the choice of hyperparameters and models plays a crucial role in their performance. Typically, these choices are determined by considering only the character (CER) and/or word error rate (WER) metrics. However, it has been shown in several studies that these metrics are largely incomplete and fail to adequately describe the downstream application of automatic transcripts. In this paper, we conduct a qualitative study on the French language that investigates the impact of subword tokenization algorithms and self-supervised learning models from different linguistic and acoustic perspectives, using a comprehensive set of evaluation metrics.

Abstract (translated)

URL

https://arxiv.org/abs/2605.03696

PDF

https://arxiv.org/pdf/2605.03696.pdf

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