Communication and language processing disorders can significantly impact the lives of millions of people. The proposed Conversations project will combine an unprecedented ECoG dataset and a cutting-edge MLbased encoding framework to test competing models for neural mechanisms supporting natural language processing and face-to-face communication in natural contexts. Uncovering the neural mechanisms behind everyday communication can give voice to people with speech and hearing impairments, shed light on communication disorders, and enhance doctor-patient communication. In Aim 1, we will create the "24/7 Conversations" dataset of 750 hrs of continuous electrocorticography (ECoG) data from epilepsy patients engaging in free daily life conversations. This dataset will be the largest collection of real-life conversations and intracranial neural activity assembled. Aim 2 involves developing an ML-based encoding framework to test the ability of various language models to model our "24/7 Conversations" dataset. ML-based encoding framework will allow us to explore brain-to-brain communication dynamics during face-to-face conversations. Finally, Aim 3 will leverage the ML-based encoding framework to construct novel computational models that simulate the neural basis of natural language processing, focusing on how the brain predicts and integrates spoken language during spontaneous real-life conversations. Through these efforts, we aim to map the intricate neural activities associated with language, providing insights into normal and disordered communication.