Frank H. Guenther

The paper investigates how basal ganglia activity encodes speech production and ambient noise levels in humans, finding distinct neural populations for each.

This paper uses machine learning models to create fast surrogates for biomechanical laryngeal control models, enabling real-time applications with reduced computational costs.

This study investigates how rhythmic speech affects neural activity in people who stutter, finding increased cerebellar connectivity during rhythmic speech.

The paper investigates cortical thickness correlations with verbal repetition tasks in PPA patients, supporting phonological working memory theories and revealing subtype-specific atrophy patterns.

The paper assesses the reliability of speech-related brain activation patterns across multiple sessions and finds high reliability using traditional and machine-learning methods.