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Diagnosis of depression by behavioural signals: a multimodal approach.

, , , , , and . AVEC@ACM Multimedia, page 11-20. ACM, (2013)

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Gaussian Process Regression for Continuous Emotion Recognition with Global Temporal Invariance., , and . AffComp@IJCAI, volume 66 of Proceedings of Machine Learning Research, page 34-44. PMLR, (2017)Empirical mode decomposition based weighted frequency feature for speech-based emotion classification., , and . ICASSP, page 5017-5020. IEEE, (2008)Compensation Techniques for Speaker Variability in Continuous Emotion Prediction., , and . IEEE Trans. Affect. Comput., 12 (2): 439-452 (2021)Cochlear Signal Processing: A Platform for Learning the Fundamentals of Digital Signal Processing., and . ICASSP, page 9229-9233. IEEE, (2020)Estimating cognitive load from speech gathered in a complex real-life training exercise., , , , , and . Int. J. Hum. Comput. Stud., (2019)Belief Mismatch Coefficient (BMC): A Novel Interpretable Measure of Prediction Accuracy for Ambiguous Emotion States., , , and . ACII, page 1-8. IEEE, (2023)Bidirectional Modelling for Short Duration Language Identification., , , and . INTERSPEECH, page 2809-2813. ISCA, (2017)Phonetic and speaker variations in automatic emotion classification., , and . INTERSPEECH, page 617-620. ISCA, (2008)Modeling spectral variability for the classification of depressed speech., , , , and . INTERSPEECH, page 857-861. ISCA, (2013)Investigation of Sub-Band Discriminative Information Between Spoofed and Genuine Speech., , , and . INTERSPEECH, page 1710-1714. ISCA, (2016)