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Multimodal Depression Detection: Fusion Analysis of Paralinguistic, Head Pose and Eye Gaze Behaviors.

, , , , , , and . IEEE Trans. Affect. Comput., 9 (4): 478-490 (2018)

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Voice source waveforms for utterance level speaker identification using support vector machines., , and . CITA, page 1-7. IEEE, (2013)A multimodal system to characterise melancholia: cascaded bag of words approach., , and . ICMI, page 274-280. ACM, (2017)Automatic frontal face annotation and AAM building for arbitrary expressions from a single frontal image only., , and . ICIP, page 2445-2448. IEEE, (2009)Emotion recognition in the wild., , , and . J. Multimodal User Interfaces, 10 (2): 95-97 (2016)R-norm: improving inter-speaker variability modelling at the score level via regression score normalisation., , and . INTERSPEECH, page 3117-3121. ISCA, (2013)An Investigation of Depressed Speech Detection: Features and Normalization., , , and . INTERSPEECH, page 2997-3000. ISCA, (2011)A systematic review of neurophysiological sensing for the assessment of acute pain., , , and . npj Digit. Medicine, (2023)An investigation of linguistic stress and articulatory vowel characteristics for automatic depression classification., , and . Comput. Speech Lang., (2019)"Where does it hurt?": Exploring EDA Signals to Detect and Localise Acute Pain., , , , , and . EMBC, page 1-5. IEEE, (2023)Explainable Depression Detection via Head Motion Patterns., , , , and . ICMI, page 261-270. ACM, (2023)