Using multiple databases for training in emotion recognition: to unite or to vote?

Björn Wolfgang Schuller · Zixing Zhang · Felix Johannes Weninger · Gerhard Rigoll

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Emotion and Mood Recognition Sentiment Analysis and Opinion Mining Face and Expression Recognition

Abstract

We present an extensive study on the performance of data agglomeration and decision-level fusion for robust cross-corpus emotion recognition.We compare joint training with multiple databases and late fusion of classifiers trained on single databases, employing six frequently used corpora of natural or elicited emotion, namely ABC, AVIC, DES, eNTERFACE, SAL, VAM, and three classifiers i. e. SVM, Random Forests, Naïve Bayes to best cover for singular effects.On average over classifier and database, data agglomeration and majority voting deliver relative improvements of unweighted accuracy by 9.0 % and 4.8 %, respectively, over single-database cross-corpus classification of arousal, while majority voting performs best for valence recognition.

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References

2009The INTERSPEECH 2009 emotion challenge 2006The eNTERFACE’05 Audio-Visual Emotion Database 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 2008Abandoning emotion classes - towards continuous emotion recognition with modelling of long-range dependencies 2009Acoustic emotion recognition: A benchmark comparison of performances 2009Being bored? Recognising natural interest by extensive audiovisual integration for real-life application

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