- Position identifier: ESR10
- Host partner: UoC
Much research effort has been invested in determining the source of the intelligibility gain of clear speech over conversational speech. Finding artefact-free modification algorithms to convert from conversational to clear speech remains a challenging problem. This project will investigate parametric ways to describe, at a range of time resolutions (from glottal cycle to higher segmental levels), the role of temporal envelope and fine structure modulations in clear and conversational speech. A parametric representation will facilitate then the design of mapping mechanisms, for instance using modern machine learning approaches, to associate parameters between the two styles. Applying the learned mapping functions, and using high-quality speech synthesis approaches (i.e., adaptive sinusoidal models) artefact-free modifications will be obtained to transform conversational speech to clear speech effectively.
The post-holder will complete a project under the supervision of Professor Yannis Stylianou and Dr. George Kafentzis, with additional supervision from Prof. Valerie Hazan (UCL, London UK), Dr. Olivier Rosec (Voxygen, France) and Dr. Chariton Papadakis (Chania General Hospital, Crete Greece).
- An undergraduate or MSc degree in a relevant discipline (e.g., computer science and/or electrical engineering, etc.)
- Strong mathematical and signal processing background.
- Experience with scientific tools and programming languages such as C, C++, C#, MATLAB and R.
- Knowledge of the relevant scientific literature on auditory signal processing will be an advantage
This position will be readvertised later in 2017 for a September 2017 start. For more details on how to apply when the position is advertised, see local web site