CCA based feature selection with application to continuous depression recognition from acoustic speech features

Heysem Kaya · Florian Eyben · Albert Ali Salah · Björn Wolfgang Schuller

63 citations0/0 files storedIEEE International Conference on Acoustics Speech and Signal Processing

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

Abstract

In this study we make use of Canonical Correlation Analysis (CCA) based feature selection for continuous depression recognition from speech. Besides its common use in multi-modal/multi-view feature extraction, CCA can be easily employed as a feature selector. We introduce several novel ways of CCA based filter (ranking) methods, showing their relations to previous work. We test the suitability of proposed methods on the AVEC 2013 dataset under the ACM MM 2013 Challenge protocol. Using 17% of features, we obtained a relative improvement of 30% on the challenge's test-set baseline Root Mean Square Error.

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References

1996Comparison of Beck Depression Inventories-IA and-II in Psychiatric Outpatients 2013The INTERSPEECH 2013 computational paralinguistics challenge: social signals, conflict, emotion, autism 2013AVEC 2013 2012Categorical and dimensional affect analysis in continuous input: Current trends and future directions 2012AVEC 2012 2013Depression recognition based on dynamic facial and vocal expression features using partial least square regression 2013Diagnosis of depression by behavioural signals 2012AVEC 2012

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2016Recognizing emotions in spoken dialogue with hierarchically fused acoustic and lexical features 2018Multimodal assessment of depression from behavioral signals 2015Emotion recognition in spontaneous and acted dialogues 2016Group happiness assessment using geometric features and dataset balancing 2017Generalized Two-Stage Rank Regression Framework for Depression Score Prediction from Speech 2016

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