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Modeling spectral variability for the classification of depressed speech.

, , , , and . INTERSPEECH, page 857-861. ISCA, (2013)

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Warped Magnitude and Phase-Based Features for Language Identification., , and . ICASSP (1), page 201-204. IEEE, (2006)Evaluation Measures for Depression Prediction and Affective Computing., , and . ICASSP, page 6610-6614. IEEE, (2019)Spectral EEG featuresfor evaluating cognitive load., , and . EMBC, page 3841-3844. IEEE, (2011)Classification of Working Memory Load Using Wavelet Complexity Features of EEG Signals., , , and . ICONIP (2), volume 7664 of Lecture Notes in Computer Science, page 692-699. Springer, (2012)Beyond Subjective Self-Rating: EEG Signal Classification of Cognitive Workload., , and . IEEE Trans. Auton. Ment. Dev., 7 (4): 301-310 (2015)Weighted pairwise Gaussian likelihood regression for depression score prediction., , , and . ICASSP, page 4779-4783. IEEE, (2015)A comparative study of different classifiers for detecting depression from spontaneous speech., , , , , , and . ICASSP, page 8022-8026. IEEE, (2013)Formant Frequencies under Cognitive Load: Effects and Classification., , , and . EURASIP J. Adv. Signal Process., (2011)Recognizing Conversational State from the Eye Using Wearable Eyewear., and . ACII, page 1-8. IEEE, (2023)Think before you speak: An investigation of eye activity patterns during conversations using eyewear., , and . Int. J. Hum. Comput. Stud., (2020)