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Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions.
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2013
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H. Cao, D. G. Cooper, M. K. Keutmann, R. C. Gur, A. Nenkova, and R. Verma, “Crema-d: Crowd-sourced emotional multimodal actors dataset,” IEEE transactions on affective computing , vol. 5, no. 4, pp. 377–390, 2014
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M. Jaiswal and E. Mower Provost, “Privacy enhanced multimodal neural representations for emotion recognition,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, pp. 7985–7993, Apr. 2020
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V. Costan and S. Devadas, “Intel sgx explained.” IACR Cryptol. ePrint Arch. , vol. 2016, no. 86, pp. 1–118, 2016
2016
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M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security , 2016, pp. 308–318
2016
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D. Bone, C.-C. Lee, T. Chaspari, J. Gibson, and S. Narayanan, “Signal processing and machine learning for mental health research and clinical applications,” IEEE Signal Processing Magazine , vol. 34, no. 5, pp. 189–196, September 2017
2017
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B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” in Artificial intelligence and statistics . PMLR, 2017, pp. 1273–1282
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R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE symposium on security and privacy (SP) . IEEE, 2017, pp. 3–18
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A. Satt, S. Rozenberg, and R. Hoory, “Efficient emotion recognition from speech using deep learning on spectrograms.” in Interspeech , 2017, pp. 1089–1093
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K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” in proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security , 2017, pp. 1175–1191
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N. Z. Gong and B. Liu, “Attribute inference attacks in online social networks,” ACM Transactions on Privacy and Security (TOPS) , vol. 21, no. 1, pp. 1–30, 2018
2018
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P. R. Kumar, P. H. Raj, and P. Jelciana, “Exploring data security issues and solutions in cloud computing,” Procedia Computer Science , vol. 125, pp. 691–697, 2018
2018
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Y. Zhang, J. Du, Z. Wang, J. Zhang, and Y. Tu, “Attention based fully convolutional network for speech emotion recognition,” in 2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) . IEEE, 2018, pp. 1771–1775
2018
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G. Ramet, P. N. Garner, M. Baeriswyl, and A. Lazaridis, “Context-aware attention mechanism for speech emotion recognition,” in 2018 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2018, pp. 126–131
2018
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S. Ling and Y. Liu, “Decoar 2.0: Deep contextualized acoustic representations with vector quantization,” 2020
2020
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A. T. Liu, S.-w. Yang, P.-H. Chi, P.-c. Hsu, and H.-y. Lee, “Mockingjay: Unsupervised speech representation learning with deep bidirectional transformer encoders,” ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , May 2020
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2020
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F. Mireshghallah, M. Taram, P. Ramrakhyani, A. Jalali, D. Tullsen, and H. Esmaeilzadeh, “Shredder: Learning noise distributions to protect inference privacy,” in Proceedings of the Twenty-Fifth International Conference on Architectural Support for Programming Languages and Operating Systems , 2020, pp. 3–18
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J. Domingo-Ferrer, O. Farras, J. Ribes-González, and D. Sánchez, “Privacy-preserving cloud computing on sensitive data: A survey of methods, products and challenges,” Computer Communications , vol. 140, pp. 38–60, 2019
2019
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L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov, “Exploiting unintended feature leakage in collaborative learning,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 691–706
2019
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M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,” in 2019 IEEE symposium on security and privacy (SP) . IEEE, 2019, pp. 739–753
2019
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2019
Cited alongside, same era.
Y.-A. Chung, W.-N. Hsu, H. Tang, and J. Glass, “An unsupervised autoregressive model for speech representation learning,” in Interspeech , 2019
2019
Cited alongside, same era.
M.-C. Lee, S.-Y. Chiang, S.-C. Yeh, and T.-F. Wen, “Study on emotion recognition and companion chatbot using deep neural network,” Multimedia Tools and Applications , vol. 79, no. 27, pp. 19 629–19 657, 2020
2020
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2020
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H. Tabrizchi and M. K. Rafsanjani, “A survey on security challenges in cloud computing: issues, threats, and solutions,” The journal of supercomputing , vol. 76, no. 12, pp. 9493–9532, 2020
2020
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K. Wei, J. Li, M. Ding, C. Ma, H. H. Yang, F. Farokhi, S. Jin, T. Q. Quek, and H. V. Poor, “Federated learning with differential privacy: Algorithms and performance analysis,” IEEE Transactions on Information Forensics and Security , vol. 15, pp. 3454–3469, 2020
2020
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2021
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S. wen Yang, P.-H. Chi, Y.-S. Chuang, C.-I. J. Lai, K. Lakhotia, Y. Y. Lin, A. T. Liu, J. Shi, X. Chang, G.-T. Lin, T.-H. Huang, W.-C. Tseng, K. tik Lee, D.-R. Liu, Z. Huang, S. Dong, S.-W. Li, S. Watanabe, A. Mohamed, and H. yi Lee, “SUPERB: Speech Processing Universal PERformance Benchmark,” in Proc. Interspeech 2021 , 2021, pp. 1194–1198
2021
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A. T. Liu, S.-W. Li, and H.-y. Lee, “Tera: Self-supervised learning of transformer encoder representation for speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 2351–2366, 2021
2021
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2021
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H. Hu, Z. Salcic, L. Sun, G. Dobbie, and X. Zhang, “Source inference attacks in federated learning,” in 2021 IEEE International Conference on Data Mining (ICDM) . IEEE, 2021, pp. 1102–1107
2021
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2021
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2021
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K. G. Narra, Z. Lin, Y. Wang, K. Balasubramanian, and M. Annavaram, “Origami inference: Private inference using hardware enclaves,” in 2021 IEEE 14th International Conference on Cloud Computing (CLOUD) . IEEE, 2021, pp. 78–84
2021
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2022
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M. Dias, A. Abad, and I. Trancoso, “Exploring hashing and cryptonet based approaches for privacy-preserving speech emotion recognition,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 2057–2061
2061
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