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Bengio, \u201cLearning phrase representations using RNN encoder-decoder for statistical machine translation,\u201d Proc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp.1724-1734, 2014, DOI: 10.3115\/ v1\/D14-1179. 10.3115\/v1\/D14-1179","DOI":"10.3115\/v1\/D14-1179"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] G.-B. Zhou, J. Wu, C.-L. Zhang, and Z.-H. Zhou, \u201cMinimal gated unit for recurrent neural networks,\u201d Int. J. Autom. Comput., vol.13, no.3, pp.226-234, 2016, DOI: 10.1007\/s11633-016-1006-2. 10.1007\/s11633-016-1006-2","DOI":"10.1007\/s11633-016-1006-2"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] J.C. Heck and F.M. Salem, \u201cSimplified minimal gated unit variations for recurrent neural networks,\u201d Midwest Symposium on Circuits and Systems, 2017, pp.1593-1596, DOI: 10.1109\/MWSCAS. 2017.8053242. 10.1109\/MWSCAS.2017.8053242","DOI":"10.1109\/MWSCAS.2017.8053242"},{"key":"6","unstructured":"[6] K. 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Black, \u201cStatistical parametric speech synthesis,\u201d Speech Commun., vol.51, no.11, pp.1039-1064, 2009, DOI: 10.1016\/j.specom.2009.04.004. 10.1016\/j.specom.2009.04.004","DOI":"10.1016\/j.specom.2009.04.004"},{"key":"9","doi-asserted-by":"publisher","unstructured":"[9] K. Tokuda, Y. Nankaku, T. Toda, H. Zen, J. Yamagishi, and K. Oura, \u201cSpeech synthesis based on hidden Markov models,\u201d Proc. IEEE, vol.101, no.5, pp.1234-1252, May 2013, DOI: 10.1109\/ JPROC.2013.2251852. 10.1109\/JPROC.2013.2251852","DOI":"10.1109\/JPROC.2013.2251852"},{"key":"10","doi-asserted-by":"publisher","unstructured":"[10] B.-W. Chen, C.-Y. Chen, and J.-F. Wang, \u201cSmart homecare surveillance system: Behavior identification based on state-transition support vector machines and sound directivity pattern analysis,\u201d IEEE Trans. Syst. Man Cybern. Syst., vol.43, no.6, pp.1279-1289, 2013, DOI: 10.1109\/TSMC.2013.2244211. 10.1109\/TSMC.2013.2244211","DOI":"10.1109\/TSMC.2013.2244211"},{"key":"11","doi-asserted-by":"publisher","unstructured":"[11] B.-W. Chen, S. Rho, M. Imran, M. Guizani, and W.-K. Fan, \u201cCognitive sensors based on ridge phase-smoothing localization and multiregional histograms of oriented gradients,\u201d IEEE Trans. Emerg. Top. Comput., vol.7, no.1, pp.123-134, 2019, DOI: 10.1109\/ TETC.2016.2585040. 10.1109\/TETC.2016.2585040","DOI":"10.1109\/TETC.2016.2585040"},{"key":"12","unstructured":"[12] G.C. Cawley and P.D. Noakes, \u201cLSP speech synthesis using backpropagation networks Rel,\u201d Third International Conference on Artificial Neural Networks, p.294, 1993, Accessed: March 13, 2018. [Online]. Available: http:\/\/ieeexplore.ieee.org\/document\/263208\/."},{"key":"13","unstructured":"[13] R. Jozefowicz, W. Zaremba, and I. 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