Details

Title

Laughter Classification Using Deep Rectifier Neural Networks with a Minimal Feature Subset

Journal title

Archives of Acoustics

Yearbook

2016

Volume

vol. 41

Issue

No 4

Authors

Keywords

speech recognition ; speech technology ; computational paralinguistics ; laughter detection ; deep neural networks

Divisions of PAS

Nauki Techniczne

Coverage

669-682

Publisher

Polish Academy of Sciences, Institute of Fundamental Technological Research, Committee on Acoustics

Date

2016

Type

Artykuły / Articles

Identifier

DOI: 10.1515/aoa-2016-0064

Source

Archives of Acoustics; 2016; vol. 41; No 4; 669-682

References

Bryant (2014), The animal nature of spontaneous human laughter Evolution and Human, Behavior, 35, 327. ; HintonG (2006), A fast learning algorithm for deep belief nets, Neural Computation, 18, 1527, doi.org/10.1162/neco.2006.18.7.1527 ; SchapireR (1999), Improved boosting algorithms using confidence - rated predictions, Machine Learning, 37, 297, doi.org/10.1023/A:1007614523901 ; HudenkoW (2009), Laughter differs in children with autism : An acoustic analysis of laughs produced by children with and without the disorder of Autism and Developmental Disorders, Journal, 39, 1392. ; SchölkopfB (2001), Estimating the support of a high - dimensional distribution, Neural Computation, 13, 1443, doi.org/10.1162/089976601750264965 ; NwokahE (1993), Vocal affect in three - year - olds : a quantitative acoustic analysis of child laughter of the Acoustical Society of America, Journal, 94, 3076. ; BachorowskiJ (2001), The acoustic features of human laughter of the Acoustical Society of America, Journal, 110, 1581. ; ChandrashekarG (2014), A survey on feature selection methods Computers & Electrical, Engineering, 40, 16. ; CampbellN (2007), On the use of nonverbal speech sounds in human communication in Proceedings of COST Action and Nonverbal Communication Behaviours pp Vietri sul Mare Italy, Verbal, 2102. ; RothgängerH (1998), Analysis of laughter and speech sounds in Italian and German students, Naturwissenschaften, 85, 394, doi.org/10.1007/s001140050522 ; Kovács (2015), Joint optimization of spectro - temporal features and Deep Neural Nets for robust automatic speech recognition, Acta Cybernetica, 22, 117, doi.org/10.14232/actacyb.22.1.2015.8 ; TruongK (2007), Automatic discrimination between laughter and speech, Speech Communication, 49, 144, doi.org/10.1016/j.specom.2007.01.001 ; HolmesJ (2002), Having a laugh at work : How humour contributes to workplace culture of Pragmatics, Journal, 34, 1683. ; LukácsE (1955), A characterization of the Gamma distribution of Mathematical, Annals Statistics, 26, 319, doi.org/10.1214/aoms/1177728549
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