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Estimating dimensional emotions, such as activation, valence and dominance, from acoustic speech signals has been widely explored over the past few years.
L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE , vol. 77, no. 2, pp. 257–286, Feb. 1989
1989
Earlier work this paper cites.
P. Ekman, “An argument for basic emotions,” Cognition & emotion , vol. 6, no. 3-4, pp. 169–200, 1992
1992
Earlier work this paper cites.
J. Posner, J. A. Russell, and B. S. Peterson, “The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology,” Development and psychopathology , vol. 17, no. 3, pp. 715–734, 2005
2005
Earlier work this paper cites.
E. Douglas-Cowie, L. Devillers, J.-C. Martin, R. Cowie, S. Savvidou, S. Abrilian, and C. Cox, “Multimodal databases of everyday emotion: Facing up to complexity,” in Ninth European conference on speech communication and technology , 2005
2005
Earlier work this paper cites.
C. Busso, M. Bulut, C.-C. Lee, A. Kazemzadeh, E. Mower, S. Kim, J. N. Chang, S. Lee, and S. S. Narayanan, “Iemocap: Interactive emotional dyadic motion capture database,” Language resources and evaluation , vol. 42, no. 4, pp. 335–359, 2008
2008
Earlier work this paper cites.
F. Seide, G. Li, and D. Yu, “Conversational speech transcription using context-dependent deep neural networks,” in Twelfth annual conference of the international speech communication association , 2011
2011
Earlier work this paper cites.
B. Schuller, M. Valster, F. Eyben, R. Cowie, and M. Pantic, “Avec 2012: the continuous audio/visual emotion challenge,” in Proceedings of the 14th ACM international conference on Multimodal interaction , 2012, pp. 449–456
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
P. Jackson and S. Haq, “Surrey audio-visual expressed emotion (savee) database,” University of Surrey: Guildford, UK , 2014
2014
Earlier work this paper cites.
S. Mariooryad, R. Lotfian, and C. Busso, “Building a naturalistic emotional speech corpus by retrieving expressive behaviors from existing speech corpora,” in Fifteenth Annual Conference of the International Speech Communication Association , 2014
2014
Earlier work this paper cites.
V. Mitra and H. Franco, “Time-frequency convolutional networks for robust speech recognition,” in 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) . IEEE, 2015, pp. 317–323
2015
Earlier work this paper cites.
F. Eyben, K. R. Scherer, B. W. Schuller, J. Sundberg, E. André, C. Busso, L. Y. Devillers, J. Epps, P. Laukka, S. S. Narayanan et al. , “The geneva minimalistic acoustic parameter set (gemaps) for voice research and affective computing,” IEEE transactions on affective computing , vol. 7, no. 2, pp. 190–202, 2015
2015
Cited alongside, same era.
Y. Chebotar and A. Waters, “Distilling knowledge from ensembles of neural networks for speech recognition.” in Interspeech , 2016, pp. 3439–3443
2016
Cited alongside, same era.
A. S. Cowen and D. Keltner, “Self-report captures 27 distinct categories of emotion bridged by continuous gradients,” Proceedings of the National Academy of Sciences , vol. 114, no. 38, pp. E7900–E7909, 2017
2017
Cited alongside, same era.
Z. Aldeneh, S. Khorram, D. Dimitriadis, and E. M. Provost, “Pooling acoustic and lexical features for the prediction of valence,” in Proceedings of the 19th ACM International Conference on Multimodal Interaction , 2017, pp. 68–72
2017
2020
Later among the works it cites.
A. Baevski, Y. Zhou, A. Mohamed, and M. Auli, “wav2vec 2.0: A framework for self-supervised learning of speech representations,” Advances in Neural Information Processing Systems , vol. 33, pp. 12 449–12 460, 2020
2020
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2020
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2021
Later among the works it cites.
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Cited alongside, same era.
R. Lotfian and C. Busso, “Building naturalistic emotionally balanced speech corpus by retrieving emotional speech from existing podcast recordings,” IEEE Transactions on Affective Computing , vol. 10, no. 4, pp. 471–483, 2017
2017
Cited alongside, same era.
R. A. Khalil, E. Jones, M. I. Babar, T. Jan, M. H. Zafar, and T. Alhussain, “Speech emotion recognition using deep learning techniques: A review,” IEEE Access , vol. 7, pp. 117 327–117 345, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Sahu, V. Mitra, N. Seneviratne, and C. Y. Espy-Wilson, “Multi-modal learning for speech emotion recognition: An analysis and comparison of asr outputs with ground truth transcription.” in Interspeech , 2019, pp. 3302–3306
2019
Cited alongside, same era.
V. Kowtha, V. Mitra, C. Bartels, E. Marchi, S. Booker, W. Caruso, S. Kajarekar, and D. Naik, “Detecting emotion primitives from speech and their use in discerning categorical emotions,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7164–7168
2020
Cited alongside, same era.
B. T. Atmaja and M. Akagi, “Dimensional speech emotion recognition from speech features and word embeddings by using multitask learning,” APSIPA Transactions on Signal and Information Processing , vol. 9, 2020
2020
Cited alongside, same era.
2021
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W.-N. Hsu, B. Bolte, Y.-H. H. Tsai, K. Lakhotia, R. Salakhutdinov, and A. Mohamed, “Hubert: Self-supervised speech representation learning by masked prediction of hidden units,” IEEE/ACM Transactions on Audio, Speech, and Language Processing , vol. 29, pp. 3451–3460, 2021
2021
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2021
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2021
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2021
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