Mixture of Informed Experts for Multilingual Speech Recognition

Multilingual speech recognition models are capable of recognizing speech in multiple different languages. When trained on related or low-resource languages, these models often outperform their monolingual counterparts. Similar to other forms of multi-task models, when the group of languages are unrelated, or when large amounts of training data is available, multilingual models can suffer from performance loss. We investigate the use of a mixture-of-expert approach to assign per-language parameters in the model to increase network capacity in a structured fashion. We introduce a novel variant of this approach, 'informed experts', which attempts to tackle inter-task conflicts by eliminating gradients from other tasks in the these task-specific parameters. We conduct experiments on a real-world task on English, French and four dialects of Arabic to show the effectiveness of our approach.

• Responsible AI

• Speech Processing