Báo cáo hóa học: " Research Article Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker "

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article Assessment of Severe Apnoea through Voice Analysis, Automatic Speech, and Speaker | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009 Article ID 982531 11 pages doi 2009 982531 Research Article Assessment of Severe Apnoea through Voice Analysis Automatic Speech and Speaker Recognition Techniques Ruben Fernandez Pozo 1 Jose Luis Blanco Murillo 1 Luis Hernandez Gomez 1 Eduardo Lopez Gonzalo 1 Jose Alcazar Ramirez 2 and Doroteo T. Toledano3 1 Signal Systems and Radiocommunications Department Universidad Politecnica de Madrid Madrid 28040 Spain 2Respiratory Department Hospital Torrecardenas Almería 04009 Spain 3ATVS Biometric Recognition Group Universidad Autonoma de Madrid Madrid 28049 Spain Correspondence should be addressed to Ruben Fernandez Pozo ruben@ Received 1 November 2008 Revised 5 February 2009 Accepted 8 May 2009 Recommended by Tan Lee This study is part of an ongoing collaborative effort between the medical and the signal processing communities to promote research on applying standard Automatic Speech Recognition ASR techniques for the automatic diagnosis of patients with severe obstructive sleep apnoea OSA . Early detection of severe apnoea cases is important so that patients can receive early treatment. Effective ASR-based detection could dramatically cut medical testing time. Working with a carefully designed speech database of healthy and apnoea subjects we describe an acoustic search for distinctive apnoea voice characteristics. We also study abnormal nasalization in OSA patients by modelling vowels in nasal and nonnasal phonetic contexts using Gaussian Mixture Model GMM pattern recognition on speech spectra. Finally we present experimental findings regarding the discriminative power of GMMs applied to severe apnoea detection. We have achieved an 81 correct classification rate which is very promising and underpins the interest in this line of inquiry. Copyright 2009 Ruben Fernandez Pozo et al. This is an open access article distributed under the Creative Commons .

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