báo cáo hóa học:" Research Article On the Impact of Children’s Emotional Speech on Acoustic and Language Models"

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 On the Impact of Children’s Emotional Speech on Acoustic and Language Models | Hindawi Publishing Corporation EURASIP Journal on Audio Speech and Music Processing Volume 2010 Article ID 783954 14 pages doi 2010 783954 Research Article On the Impact of Children s Emotional Speech on Acoustic and Language Models Stefan Steidl 1 Anton Batliner 1 Dino Seppi 2 and Bjorn Schuller3 1Lehrstuhl fur Mustererkennung Friedrich-Alexander-UniversitatErlangen-Nurnberg Martensstrafie 3 91058 Erlangen Germany 2ESAT Katholieke Universiteit Leuven Kasteelpark Arenberg 10 3001 Heverlee Leuven Belgium 3 Institute for Human-Machine Communication Technische Universitat Munchen Arcisstrafie 21 80333 Munchen Germany Correspondence should be addressed to Stefan Steidl Received 2 June 2009 Revised 9 October 2009 Accepted 23 November 2009 Academic Editor Georg Stemmer Copyright 2010 Stefan Steidl 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. The automatic recognition of children s speech is well known to be a challenge and so is the influence of affect that is believed to downgrade performance of a speech recogniser. In this contribution we investigate the combination of both phenomena. Extensive test runs are carried out for 1 k vocabulary continuous speech recognition on spontaneous motherese emphatic and angry children s speech as opposed to neutral speech. The experiments address the question how specific emotions influence word accuracy. In a first scenario emotional speech recognisers are compared to a speech recogniser trained on neutral speech only. For this comparison equal amounts of training data are used for each emotion-related state. In a second scenario a neutral speech recogniser trained on large amounts of neutral speech is adapted by adding only some emotionally coloured data in the training process. The results show that emphatic and

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