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 Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2011 Article ID 838790 16 pages doi 2011 838790 Research Article Recognition of Nonprototypical Emotions in Reverberated and Noisy Speech by Nonnegative Matrix Factorization Felix Weninger 1 Bjorn Schuller 1 Anton Batliner 2 Stefan Steidl 2 and Dino Seppi3 1Lehrstuhl fiir Mensch-Maschine-Kommunikation Technische Universitat Munchen 80290 Munchen Germany 2Mustererkennung Labor Friedrich-Alexander-Universitiit Erlangen-Nurnberg 91058 Erlangen Germany 3ESAT Katholieke Universiteit Leuven 3001 Leuven Belgium Correspondence should be addressed to Felix Weninger weninger@ Received 30 July 2010 Revised 15 November 2010 Accepted 18 January 2011 Academic Editor Julien Epps Copyright 2011 Felix Weninger et al. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. We present a comprehensive study on the effect of reverberation and background noise on the recognition of nonprototypical emotions from speech. We carry out our evaluation on a single well-defined task based on the FAU Aibo Emotion Corpus consisting of spontaneous children s speech which was used in the INTERSPEECH 2009 Emotion Challenge the first of its kind. Based on the challenge task and relying on well-proven methodologies from the speech recognition domain we derive test scenarios with realistic noise and reverberation conditions including matched as well as mismatched condition training. As feature extraction based on supervised Nonnegative Matrix Factorization NMF has been proposed in automatic speech recognition for enhanced robustness we introduce and evaluate different kinds of NMF-based features for emotion recognition. We conclude that NMF features can significantly contribute to the robustness of state-of-the-art emotion recognition engines in .
