Research Associate/Research Fellow – Natural Speech Technology

Fixed-term for 3 years

Research Associate – Grade 7: £28,251 to £35,788 per annum
Research Fellow – Grade 8: £36,862 to £44,016 per annum

Closing Date
6 December 2011

The Speech and Hearing research group in the Department of Computer Science (SPandH) is a partner in the EPSRC Programme Grant in Natural Speech Technology (NST), in collaboration with the Universities of Edinburgh and Cambridge. NST is a large and ambitious project, aiming to significantly advance the state-of-the-art in speech technology by making it more natural, approaching human levels of reliability, adaptability and conversational richness. The total duration of the NST programme is 5 years and it is organised in themes that cover a diverse set of collaborative studies in speech recognition and synthesis. Applications, practical demonstrations and interaction with technology users in industry are also part of the programme. The successful applicant will work on speech recognition research topics under the NST programme at Sheffield.

SPandH has developed state-of-the-art automatic speech recognition systems that have repeatedly shown best performance in international competitions (U.S. NIST) and are publicly available ( In clinical applications, SPandH has introduced a user-driven methodology for personalised speech technology. Together, these advances form the foundation for Sheffield work within NST. Excellent computing resources are available to allow ambitious experiments with innovative ideas. This is an opportunity to work in a well-connected international team with world-leading reputations in speech recognition research and in collaboration with outstanding groups at the Centre for Speech Technology Research at Edinburgh and the Machine Intelligence Lab at Cambridge University.

Applicants should have a PhD (or have equivalent experience) in a related subject area. Applicants are required to have a good track record in research of speech recognition and/or machine learning topics. Experience in one or more of the following areas will be an advantage:

statistical machine learning ,
pattern processing
signal processing
acoustic or language modelling for automatic speech recognition

Solid knowledge of Unix type operating systems and programming in C/C++ is required. For an appointment at Research Fellow level, experience in research management is essential as candidates are expected to take a leading role in site scientific management.

For further information see

For informal enquiries please contact Thomas Hain (t.hain(at) or Phil Green (