On Acoustic Emotion Recognition: Compensating for Covariate Shift

Ali Hassan · R.I. Damper · Mahesan Niranjan

112 citations0/0 files storedIEEE Transactions on Audio Speech and Language Processing

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

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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References

2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 1993Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion 2009The INTERSPEECH 2009 emotion challenge 2011Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge 2008The Vera am Mittag German audio-visual emotional speech database 2009OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit 2010Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies

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