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Using multiple databases for training in emotion recognition: To unite or to vote?

, , , and . Interspeech, page 1553--1556. (2011)

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Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition., and . INTERSPEECH, page 362-365. ISCA, (2012)Towards Automatic Intoxication Detection from Speech in Real-Life Acoustic Environments., , and . ITG Conference on Speech Communication, page 1-4. IEEE, (2012)Leveraging Unlabeled Data for Emotion Recognition With Enhanced Collaborative Semi-Supervised Learning., , , , , and . IEEE Access, (2018)Dynamic Difficulty Awareness Training for Continuous Emotion Prediction., , , and . CoRR, (2018)Snoring classified: The Munich-Passau Snore Sound Corpus., , , , , , , , , and 1 other author(s). Comp. in Bio. and Med., (2018)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)Implicit Fusion by Joint Audiovisual Training for Emotion Recognition in Mono Modality., , , and . ICASSP, page 5861-5865. IEEE, (2019)A Parallel Framework for Large-scale Multidimensional Heterogeneous Network Analysis., , and . ASONAM, page 625-626. ACM, (2017)Dynamic Active Learning Based on Agreement and Applied to Emotion Recognition in Spoken Interactions., , , , and . ICMI, page 275-278. ACM, (2015)