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Báo cáo hóa học: " Research Article Automated Intelligibility Assessment of Pathological Speech Using Phonological Features"

RTuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: esearch Article Automated Intelligibility Assessment of Pathological Speech Using Phonological Features | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 629030 9 pages doi 10.1155 2009 629030 Research Article Automated Intelligibility Assessment of Pathological Speech Using Phonological Features Catherine Middag 1 Jean-Pierre Martens 1 Gwen Van Nuffelen 2 and Marc De Bodt2 1 Department of Electronics and Information Systems Ghent University 9000 Ghent Belgium 2 Antwerp University Hospital University of Antwerp 2650 Edegem Belgium Correspondence should be addressed to Catherine Middag catherine.middag@ugent.be Received 31 October 2008 Accepted 24 March 2009 Recommended by Juan I. Godino-Llorente It is commonly acknowledged that word or phoneme intelligibility is an important criterion in the assessment of the communication efficiency of a pathological speaker. People have therefore put a lot of effort in the design of perceptual intelligibility rating tests. These tests usually have the drawback that they employ unnatural speech material e.g. nonsense words and that they cannot fully exclude errors due to listener bias. Therefore there is a growing interest in the application of objective automatic speech recognition technology to automate the intelligibility assessment. Current research is headed towards the design of automated methods which can be shown to produce ratings that correspond well with those emerging from a well-designed and well-performed perceptual test. In this paper a novel methodology that is built on previous work Middag et al. 2008 is presented. It utilizes phonological features automatic speech alignment based on acoustic models that were trained on normal speech context-dependent speaker feature extraction and intelligibility prediction based on a small model that can be trained on pathological speech samples. The experimental evaluation of the new system reveals that the root mean squared error of the discrepancies between perceived and computed intelligibilities can be as low as 8 on a .