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Processing negative emotions through social communication: Multimodal database construction and analysis.

, , , , and . ACII, page 79-85. IEEE Computer Society, (2017)

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Iterative training of a DPGMM-HMM acoustic unit recognizer in a zero resource scenario., , and . SLT, page 57-63. IEEE, (2016)Out-of-Task Training for Dialog State Tracking Models., , , , , , and . COLING, page 6767-6774. International Committee on Computational Linguistics, (2020)The NAIST ASR system for the 2015 Multi-Genre Broadcast challenge: On combination of deep learning systems using a rank-score function., , , , , and . ASRU, page 654-659. IEEE, (2015)CAMELL: Confidence-based Acquisition Model for Efficient Self-supervised Active Learning with Label Validation., , , , , , , , and . CoRR, (2023)Supervised Learning of Acoustic Models in a Zero Resource Setting to Improve DPGMM Clustering., , and . INTERSPEECH, page 1310-1314. ISCA, (2016)Domain-independent User Simulation with Transformers for Task-oriented Dialogue Systems., , , , , , , and . SIGDIAL, page 445-456. Association for Computational Linguistics, (2021)Knowing What You Know: Calibrating Dialogue Belief State Distributions via Ensembles., , , , , , and . EMNLP (Findings), volume EMNLP 2020 of Findings of ACL, page 3096-3102. Association for Computational Linguistics, (2020)Dirichlet Process Mixture of Mixtures Model for Unsupervised Subword Modeling., , and . IEEE ACM Trans. Audio Speech Lang. Process., 26 (11): 2027-2042 (2018)Learning Supervised Feature Transformations on Zero Resources for Improved Acoustic Unit Discovery., , and . IEICE Trans. Inf. Syst., 101-D (1): 205-214 (2018)ChatGPT for Zero-shot Dialogue State Tracking: A Solution or an Opportunity?, , , , , , , , and . ACL (2), page 936-950. Association for Computational Linguistics, (2023)