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Learning Discriminative Features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition.

S. Tripathi, A. Ramesh, A. Kumar, C. Singh, and P. Yenigalla. AffComp@IJCAI, volume 122 of Proceedings of Machine Learning Research, page 44-53. PMLR, (2019)

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Weakly supervised hierarchical multi-task classification of customer questions.J. Rana, P. Yenigalla, C. Aggarwal, S. Mukku, M. Soni, and R. Patange. ACL (industry), page 786-793. Association for Computational Linguistics, (2023)Multi-Head Attention for Speech Emotion Recognition with Auxiliary Learning of Gender Recognition.A. Nediyanchath, P. Paramasivam, and P. Yenigalla. ICASSP, page 7179-7183. IEEE, (2020)D-Extract: Extracting Dimensional Attributes From Product Images.P. Ghosh, N. Wang, and P. Yenigalla. WACV, page 3630-3638. IEEE, (2023)InsightNet : Structured Insight Mining from Customer Feedback.S. Mukku, M. Soni, C. Aggarwal, J. Rana, P. Yenigalla, R. Patange, and S. Mohan. EMNLP (Industry Track), page 552-566. Association for Computational Linguistics, (2023)Phoneme based Domain Prediction for Language Model Adaptation.A. Bhasin, G. Mathur, P. Yenigalla, and B. Natarajan. IJCNN, page 1-6. IEEE, (2020)Product Image Representation Learning on Large Scale Noisy Datasets.A. Joshi, N. Das, and P. Yenigalla. ICIP, page 2570-2574. IEEE, (2023)MAPS: Multimodal Attention for Product Similarity.N. Das, A. Joshi, P. Yenigalla, and G. Agrwal. WACV, page 2988-2996. IEEE, (2022)AMUSED: A Multi-Stream Vector Representation Method for Use in Natural Dialogue.G. Kumar, R. Joshi, J. Singh, and P. Yenigalla. CoRR, (2019)Speech Emotion Recognition Using Spectrogram & Phoneme Embedding.P. Yenigalla, A. Kumar, S. Tripathi, C. Singh, S. Kar, and J. Vepa. INTERSPEECH, page 3688-3692. ISCA, (2018)A Practitioners' Guide to Transfer Learning for Text Classification using Convolutional Neural Networks.T. Semwal, P. Yenigalla, G. Mathur, and S. Nair. SDM, page 513-521. SIAM, (2018)

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Learning Discriminative Features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Learning Discriminative Features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition.

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Learning Discriminative Features using Center Loss and Reconstruction as Regularizer for Speech Emotion Recognition.