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Acoustic Model Training For Non-Audible Murmur Recognition Using Transformed Normal Speech Data

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dc.contributor.author Denis Babani en
dc.contributor.author Tomoki Toda en
dc.contributor.author Hiroshi Saruwatari en
dc.contributor.author Kiyohiro Shikano en
dc.date.accessioned 2013-11-20T02:30:32Z en
dc.date.available 2013-11-20T02:30:32Z en
dc.date.issued 2011 en
dc.identifier.isbn 9781457705380 en
dc.identifier.issn 1520-6149 en
dc.identifier.uri http://hdl.handle.net/10061/9205 en
dc.description ICASSP2011: The 36th International Conference on Acoustics, Speech, and Signal Processing, May 22-27, 2011, Prague, Czech Republic. en
dc.description.abstract In this paper we present a novel approach to acoustic model training for non-audible murmur (NAM) recognition using normal speech data transformed into NAM data. NAM is extremely soft murmur, that is so quiet that people around the speaker can hardly hear it. It is detected directly through the soft tissue of the head using a special body-conductive microphone, NAM microphone, placed on the neck below the ear. NAM recognition is one of the promising silent speech interfaces for man-machine speech communication. We have previously shown the effectiveness of speaker adaptive training (SAT) based on constrained maximum likelihood linear regression (CMLLR) in NAM acoustic model training. However, since the amount of available NAM data is still small, the effect of SAT is limited. In this paper we propose modified SAT methods capable of using a larger amount of normal speech data by transforming them into NAM data. The experimental results demonstrate that the pro posed methods yield an absolute increase of approximately 2% in word accuracy compared with the conventional method. en
dc.language.iso en en
dc.publisher IEEE en
dc.rights Copyright c 2011 IEEE en
dc.subject silent speech interfaces en
dc.subject non-audible murmur recognition en
dc.subject acoustic model en
dc.subject speaker adaptive training en
dc.subject transformed normal speech en
dc.title Acoustic Model Training For Non-Audible Murmur Recognition Using Transformed Normal Speech Data en
dc.type.nii Conference Paper en
dc.textversion Publisher en
dc.identifier.ncid BB06651782 en
dc.identifier.spage 5224 en
dc.identifier.epage 5227 en
dc.relation.doi 10.1109/ICASSP.2011.5947535 en
dc.identifier.NAIST-ID 73292716 en


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