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PhD studentship (Sheffield): Metamodelling of Speech Domains – deadline 19 Apr 2026
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Applications are invited for a fully-funded 3.5 year PhD studentship on Metamodelling of Speech Domains with a September 2026 start hosted in the School of Computer Science, University of Sheffield. Full project description below.
Home and International students are eligible to apply. Regardless of fees status (Home or International), all fees will be paid in addition to an enhanced stipend and a research and training support grant to cover research expenses and conference attendance.
The deadline for applications is 19 April 2026.
For More Information, Please Visit Our Website
https://slt-cdt.sheffield.ac.uk/apply
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Project Description: Metamodelling of Speech Domains
Speech is a highly variable signal that is often recorded in complex environments and under sub-optimal conditions. The information contained in a recorded speech signal is not limited to just the words spoken; the signal also includes, for example, information on speaker identity or conversation style. Depending on the task at hand, different aspects of the speech signal are important, leading to different models being used.
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However, in recent years model topologies for automatic speech recognition and many other speech processing tasks have started to converge – driven by research focus on generalisation. Still, the issue of domain dependence often remains. Recently there has been an increased interest in model combination and model editing, for example through disentanglement of so-called task vectors.
In this project we aim to explore how different aspects of speech data are expressed in model space, in the context of automatic speech recognition and diarisation. The objective of this work is to explore methods to attribute elements of model spaces to skills, or specific aspects of the data. This can be used either as input in hypermodelling, where new models for specific domains are generated, or for improved structuring in model training and design.
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Work on this project will require research into novel methods to represent model variations and attribute them to specific attributes and tasks. The value of such models should then be demonstrated by informing training and inference processes. A range of different strategies can be explored, including new ways to derive model distributions and model parameter predictions. Experiments should be conducted on a range of tasks of different complexity in the context of different data domains, for example speech classification, speech recognition, and diarisation.
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