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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).

Record type: Article


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.

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e-pub ahead of print date: 27 March 2013
Published date: July 2013
Organisations: Southampton Wireless Group


Local EPrints ID: 350948
ISSN: 1558-7916
PURE UUID: 3fa3b776-6e18-4a3c-8b98-e194157b06ea

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Date deposited: 11 Apr 2013 09:57
Last modified: 18 Jul 2017 04:29

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Author: A. Hassan
Author: R.I. Damper
Author: M. Niranjan

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