DocumentCode :
3486206
Title :
Unsupervised learning in cross-corpus acoustic emotion recognition
Author :
Zhang, Zixing ; Weninger, Felix ; Wöllmer, Martin ; Schuller, Björn
Author_Institution :
Inst. for Human-Machine Commun., Tech. Univ. Munchen, München, Germany
fYear :
2011
fDate :
11-15 Dec. 2011
Firstpage :
523
Lastpage :
528
Abstract :
One of the ever-present bottlenecks in Automatic Emotion Recognition is data sparseness. We therefore investigate the suitability of unsupervised learning in cross-corpus acoustic emotion recognition through a large-scale study with six commonly used databases, including acted and natural emotion speech, and covering a variety of application scenarios and acoustic conditions. We show that adding unlabeled emotional speech to agglomerated multi-corpus training sets can enhance recognition performance even in a challenging cross-corpus setting; furthermore, we show that the expected gain by adding unlabeled data on average is approximately half the one achieved by additional manually labeled data in leave-one-corpus-out validation.
Keywords :
acoustic signal processing; emotion recognition; speech recognition; unsupervised learning; acted emotion speech; automatic emotion recognition; cross-corpus acoustic emotion recognition; data sparseness; leave-one-corpus-out validation; multicorpus training sets; natural emotion speech; unlabeled emotional speech; unsupervised learning; Acoustics; Databases; Emotion recognition; Speech; Speech recognition; Training; Unsupervised learning; speech emotion recognition; unsupervised learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Automatic Speech Recognition and Understanding (ASRU), 2011 IEEE Workshop on
Conference_Location :
Waikoloa, HI
Print_ISBN :
978-1-4673-0365-1
Electronic_ISBN :
978-1-4673-0366-8
Type :
conf
DOI :
10.1109/ASRU.2011.6163986
Filename :
6163986
Link To Document :
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