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Deep Learning for Mobile Mental Health: Challenges and recent advances.

, , , , , , and . IEEE Signal Process. Mag., 38 (6): 96-105 (2021)

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Implicit Fusion by Joint Audiovisual Training for Emotion Recognition in Mono Modality., , , and . ICASSP, page 5861-5865. IEEE, (2019)Dynamic Difficulty Awareness Training for Continuous Emotion Prediction., , , and . CoRR, (2018)Can Machine Learning Assist Locating the Excitation of Snore Sound? A Review., , , , , , , and . IEEE J. Biomed. Health Informatics, 25 (4): 1233-1246 (2021)Identifying surgical-mask speech using deep neural networks on low-level aggregation., , , , and . SAC, page 580-585. ACM, (2021)Dynamic Active Learning Based on Agreement and Applied to Emotion Recognition in Spoken Interactions., , , , and . ICMI, page 275-278. ACM, (2015)A hierarchical insurance recommendation framework using GraphOLAM approach., , , , and . ASONAM, page 757-764. ACM, (2019)Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition., and . INTERSPEECH, page 362-365. ISCA, (2012)Automated Classification of Children's Linguistic versus Non-Linguistic Vocalisations., , , and . INTERSPEECH, page 2588-2592. ISCA, (2018)Exploring Spatio-Temporal Representations by Integrating Attention-based Bidirectional-LSTM-RNNs and FCNs for Speech Emotion Recognition., , , , , and . INTERSPEECH, page 272-276. ISCA, (2018)GCFormer: A Graph Convolutional Transformer for Speech Emotion Recognition., , , and . ICMI, page 307-313. ACM, (2023)