On acoustic emotion recognition: compensating for covariate shift

Hassan, A., Damper, R.I. and Niranjan, M. (2013) On acoustic emotion recognition: compensating for covariate shift IEEE Transactions on Audio, Speech and Language Processing, 21, (7), pp. 1458-1468. (doi:10.1109/TASL.2013.2255278).


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Pattern recognition tasks often face the situation that training data are not fully representative of test data. This problem is well-recognized in speech recognition, where methods like cepstral mean normalization (CMN), vocal tract length normalization (VTLN) and maximum likelihood linear regression (MLLR) are used to compensate for channel and speaker differences. Speech emotion recognition (SER) is an important emerging field in human-computer interaction and faces the same data shift problems, a fact which has been generally overlooked in this domain. In this paper, we show that compensating for channel and speaker differences can give significant improvements in SER by modelling these differences as a covariate shift. We employ three algorithms from the domain of transfer learning that apply importance weights (IWs) within a support vector machine classifier to reduce the effects of covariate shift. We test these methods on the FAU Aibo Emotion Corpus, which was used in the Interspeech 2009 Emotion Challenge. It consists of two separate parts recorded independently at different schools; hence the two parts exhibit covariate shift. Results show that the IW methods outperform combined CMN and VTLN and significantly improve on the baseline performance of the Challenge. The best of the three methods also improves significantly on the winning contribution to the Challenge.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TASL.2013.2255278
ISSNs: 1558-7916 (print)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Organisations: Southampton Wireless Group
ePrint ID: 350948
Date :
Date Event
27 March 2013e-pub ahead of print
July 2013Published
Date Deposited: 11 Apr 2013 09:57
Last Modified: 17 Apr 2017 15:44
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/350948

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