Automatic spoken language identification utilizing acoustic and phonetic speech information

Automatic spoken Language Identi¯cation (LID) is the process of identifying the language spoken within an utterance. The challenge that this task presents is that no prior information is available indicating the content of the utterance or the identity of the speaker. The trend of globalization and the pervasive popularity of the Internet will amplify the need for the capabilities spoken language identi¯ca- tion systems provide. A prominent application arises in call centers dealing with speakers speaking di®erent languages. Another important application is to index or search huge speech data archives and corpora that contain multiple languages. The aim of this research is to develop techniques targeted at producing a fast and more accurate automatic spoken LID system compared to the previous National Institute of Standards and Technology (NIST) Language Recognition Evaluation. Acoustic and phonetic speech information are targeted as the most suitable fea- tures for representing the characteristics of a language. To model the acoustic speech features a Gaussian Mixture Model based approach is employed. Pho- netic speech information is extracted using existing speech recognition technol- ogy. Various techniques to improve LID accuracy are also studied. One approach examined is the employment of Vocal Tract Length Normalization to reduce the speech variation caused by di®erent speakers. A linear data fusion technique is adopted to combine the various aspects of information extracted from speech. As a result of this research, a LID system was implemented and presented for evaluation in the 2003 Language Recognition Evaluation conducted by the NIST.

A research opportunity : “3D-ROSE” : virtual reality preparation for children having radiotherapy or imaging procedures

Professor Christian Langton is a medical physicist at Queensland University of Technology in Brisbane. He has developed a way of preparing children who are about to have either radiotherapy or MRI imaging procedures and is seeking research partners to develop and test these further. This is a great opportunity for nurses interested in research, and who have access to a children’s hospital, to work with Professor Langton on some truly innovative, multidisciplinary research.

Paediatric spinal deformity research at the Mater Children's Hospital, Brisbane, Australia.

The Paediatric Spine Research group was formed in 2002 to perform high quality research into the prevention and management of spinal deformity, with an emphasis on scoliosis. The group has successfully built collaborative bridges between the scientific and research expertise at QUT, and the clinical skills and experience of the spinal orthopaedic surgeons at the Mater Children’s Hospital in Brisbane. Clinical and biomechanical research is now possible as a result of the development of detailed databases of patients who have innovative and unique surgical interventions for spinal deformity such as thoracoscopic scoliosis correction, thoracoscopic staple insertion for juvenile idiopathic scoliosis and minimally invasive growing rods. The Mater in Brisbane provides these unique datasets of spinal deformity surgery patients, whose procedures are not being performed anywhere else in the Southern Hemisphere. The most detailed is a database of thoracoscopic scoliosis correction surgery which now contains 180 patients with electronic collections of X-Rays, photographs and patient questionnaires. With ethics approval, a subset of these patients has had CT scans, and a further subset have had MRI scans with and without a compressive load to simulate the erect standing position. This database has to date contributed to 17 international refereed journal papers, a further 7 journal papers either under review or in final preparation, 53 national conference presentations and 35 international conference presentations. Major findings from selected journal publications will be presented. It is anticipated that as the surgical databases grow they will continue to provide invaluable clinical data which will feed into clinically relevant projects driven by both medical and engineering researchers whose findings will benefit spinal deformity patients and scientific knowledge worldwide.