Understanding speech in noisy backgrounds requires selective attention to a particular speaker. Humans excel at this challenging task, while current speech recognition technology still struggles when background noise is loud. The neural mechanisms by which we process speech remain, however, poorly understood, not least due to the complexity of natural speech. Here we describe recent progress obtained through applying machine-learning to neuroimaging data of humans listening to speech in different types of background noise. In particular, we develop statistical models to relate characteristic features of speech such as pitch, amplitude fluctuations and linguistic surprisal to neural measurements. We find neural correlates of speech processing both at the subcortical level, related to the pitch, as well as at the cortical level, related to amplitude fluctuations and linguistic structures. We also show that some of these measures allow to diagnose disorders of consciousness. Our findings may be applied in smart hearing aids that automatically adjust speech processing to assist a user, as well as in the diagnosis of brain disorders.

Irina is a research scientist at DeepMind, where she works in the Froniers team. Her work aims to bring together insights from the fields of neuroscience and physics to advance general artificial intelligence through improved representation learning. Before joining DeepMind, Irina was a British Psychological Society Undergraduate Award winner for her achievements as an undergraduate student in Experimental Psychology at Westminster University, followed by a DPhil at the Oxford Centre for Computational Neuroscience and Artificial Intelligence, where she focused on understanding the computational principles underlying speech processing in the auditory brain. During her DPhil, Irina also worked on developing poker AI, applying machine learning in the finance sector, and working on speech recognition at Google Research."" https://arxiv.org/pdf/2006.14304.pdf