This paper proposes conflict-aware multi-objective training strategies for multilingual automatic speech recognition and speech translation by selectively aligning gradients from conflicting layers to enhance efficiency and performance.

We present an Intent Classification Dataset for Wolof language. We conduct experiments on various baselines, including text and voice state-of-the-art models.