-- Research Associate in Robust Speech Recognition
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The University of Sheffield, Department of Computer Science invite applications for a position as Research Associate to work on a project to research and develop robust technology for recognition of speech in meetings.  The associated project, DocuMeet, is funded by the European Union and involves collaboration with partners from academia and industry across Europe. The Speech and Hearing Research Group is responsible for speech technology in the project, but also contributes to some natural language understanding tasks.

Speech transcription of meeting data is a well-established task, with international competitions (held by U.S. NIST), and supported by several large-scale research projects. While significant progress has been made, the performance of recognition, detection and analysis systems is still very far from usable in many realistic, natural scenarios. There are many significant challenges seeking a solution: the acoustic complexity of meetings goes well beyond standard settings.: Noise and reverberation are standard; speech signals show significant amounts of overlap between speakers; varying degrees of emotion are present; and speakers are moving. All of these pose significant challenges to speech research and practical applications.

In the DocuMeet project we specifically work on speech recognition robustness to noise and reverberation. We aim to work on new algorithms that allow to factor environment and context in novel ways (e.g. eigen-environments).  The recordings from multiple microphones can be used to remove unwanted acoustics, while knowledge about a specific environment type should be used to adjust acoustic models of the recognition systems. Further we will investigate how such algorithms can be integrated with personalisation (acoustic/language) and how metadata can be used to inform such processes. Extensive experimentation of existing and new corpora will be required to demonstrate the effectiveness of the new techniques.

Applicants are required to have a track record of work on speech technologies including speech recognition, and to have had exposure to modern machine learning techniques. Ideally, such a track record is demonstrated by publications in international journals and conferences. The successful candidate will be required to hold a PhD in the field; work on the project will require publication of results, travelling to conferences and extensive visits to itslanguage offices. At this point the project duration is for one year, but extensions are likely.
The project will be embedded in the Speech and Hearing (SpandH) research group at (http://www.dcs.shef.ac.uk/spandh) in the Department of Computer Science, and in particular the subgroup on machine intelligence for natural interfaces (MINI). SpandH is amongst the largest speech research groups in the UK, with extensive infrastructure and a vibrant research environment. The group is well known internationally for its research, which reaches across traditional divides to encompass and link computational hearing, speech perception and speech technology. The MINI subgroup is led by Prof. Hain, currently has 13 members, and is amongst other things well known for speech recognition and classification. It has had systems with best performance in international competitions that are available to the public at www.webasr.org. The subgroup is currently involved in many projects, including an EPSRC programme grant (with Univ. of Cambridge, Univ. of Edinburgh), research organisations (e.g. Idiap, NICT), and Industry (e.g. Cisco, Google). It has its own extensive computing infrastructure, access to large quantities of data, as well as dedicated recording facilities.

The Department of Computer Science, which is a member of the Faculty of Engineering, was established in 1982 and has since attained an international reputation for its research and teaching. Currently there are over 100 members of staff in Computer Science, including 35 Academics. The Department has an international reputation for the quality of its research, and was awarded grade 5 in the 2001 research assessment exercise, and in the 2008 exercise, 65% of our research was rated world leading or internationally excellent in terms of its originality, significance and rigor.

If you would like to know more about this position, please contact Prof. Thomas Hain - t.hain@dcs.shef.ac.uk.

In order to apply, the best option is to visit jobs.ac.uk and then press the 'Apply' button on the page:

  http://www.jobs.ac.uk/job/AFU678/research-associate/

The University of Shefield JOB ID is UOS005891.