Senior Research Associate in Machine Learning Speech Processing

About the Project

This Royal Society-funded project seeks to delve into the 'black box' of modern machine learning models that predict vocal tract shapes from audio recordings. The goal is to understand the intricate mapping between vocal tract movements and the acoustic speech signal. Utilizing state-of-the-art MRI recordings of the vocal tract during speech, the project aims to develop machine learning approaches that not only predict acoustic output but also reveal the underlying mechanisms.

This requires hybrid machine learning (ML) approaches that integrate phonetic and physical domain knowledge with data-driven learning, alongside explainable AI (xAI) techniques to ensure model transparency and scientific validity. These approaches will be applied to a large database of real-time MRI and acoustic recordings. The outcomes will drive fundamental progress in critical applications like articulatory biofeedback for language learning and speech therapy.

Working alongside Dr. Sam Kirkham (Lancaster, Speech Science), Dr. Anton Ragni (Sheffield, Computer Science), and Professor Aneta Stefanovska (Lancaster, Physics), you will be responsible for developing and validating interpretable Machine Learning approaches to model acoustic-articulatory relationships using MRI vocal tract data. This full-time position is available for 18 months, starting from July 1, 2026 (start date negotiable).

• Develop hybrid ML architectures that incorporate phonetic and physical constraints.
• Apply and extend explainable AI techniques for speech production modelling.
• Validate model interpretations against established knowledge.
• Lead and/or contribute to publications at the intersection of speech science and machine learning.

This role offers significant methodological creativity and intellectual ownership, providing access to rich MRI datasets and Lancaster's high-performance computing facilities.

• Intellectual ownership – you will shape the methodology, not just implement it.
• Interdisciplinary impact – apply ML expertise to fundamental questions about human speech.
• Publication opportunities – strong potential for first-author papers bridging AI and speech science.
• Flexibility – flexible working arrangements are available.
• Strong mentorship – collaborate with researchers specializing in speech production, computational modelling, machine learning, and biophysics.
• Career development – build expertise at the growing intersection of interpretable AI and speech processing.