Microscopic Intelligibility Modeling – MIM

Our project has two goals: a) make machines recognise speech more like humans do, and b) validate our understanding about human speech perception through the use of data-driven techniques. MIM aims at proposing computational models that predict human speech recognition at a fine resolution. Current approaches to intelligibility prediction provide macroscopic estimates consisting of aggregates over many stimuli and listeners. By leveraging recent developments in the field of Artificial Intelligence, models could predict recognition at a sub-lexical level. Deep learning (DL) has improved automatic speech recognition performance significantly, achieving super-human transcription in conversational tasks. We plan to build DL models to predict human listening tests responses aiming at improving individualization of hearing solutions. Scarcity and variability of human listening data, and the interpretation problem in DL are two of the main issues that we will tackle.