  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.
