Summary:

The "Bayesian Cognition and Machine Learning for Speech communication" chair, a part of the Grenoble MIAI institute, brings together researchers whose areas of expertise are in the fields of Speech Communication, Cognition, Machine Learning, and Probabilistic Modeling of sensorimotor systems. The team members come from Gipsa-lab (UMR CNRS 5216) and LPNC (UMR CNRS 5105), two labs at UGA and Grenoble INP-UGA. The aim of the chair is to build a global computational model of speech production and speech perception, that is, a system able to learn how to speak and to perceive speech from examples provided by the environment. To this purpose, an original approach is proposed, which associates the algorithmic and mathematical framework of data-based Deep Learning and hypothesis-driven Probabilistic Modelling. This approach was developed in order to design more interpretable and thus more explainable and transferrable models, with more rapid and economical implementations and more robustness and versatility. Our aim is to build models of speech communication that reach the state-of-the-art performance of current deep-learning based systems while drastically limiting the amount of training data.