Vers la compréhension automatique de la parole bout-en-bout à moindre effort

Abstract

Recent advances in spoken language understanding benefited from Self-Supervised models trained on large speech corpora. For French, the LeBenchmark project has made such models available and has led to impressive progress on several tasks including spoken language understanding. These advances have a non-negligible cost in terms of computation time and energy consumption. In this paper, we compare several learning strategies aiming at reducing such cost while keeping competitive performances. The experiments are performed on the MEDIA corpus, and show that it is possible to reduce the learning cost while maintaining state-of-the-art performances.

Language: French; Paper accepted for publication at the French Conference TALN 2022; preliminary work for the Interspeech 2022 paper (coming soon)