Amirgaliev Y., Hahn M., Mussabayev T.: The speech signal segmentation algorithm using pitch synchronous analysis. Journal Open Computer Science 7(1)/2017, 1–8.
DOI: https://doi.org/10.1515/comp-2017-0001
Andrychowicz M., Denil M., Colmenarejo S.G., Hoffman M.W., Pfau D., Schaul T., Shillingford B., de Freitas N.: Learning to learn by gradient descent by gradient descent. 30th Conference on Neural Information Processing Systems NIPS 2016.
Bahdanau D., Cho K., Bengio Y.: Neural machine translation by jointly learning to align and translate. Proc. ICLR, 2015.
Bengio Y., Ducharme R., Vincent P., Jauvin C.: A Neural Probabilistic Language Model. Journal of Machine Learning Research 3/2003, 1137–1155.
Bottou L.: Large-Scale Machine Learning with Stochastic Gradient Descent. NEC Labs America, Princeton.
Duchi J., Hazan E., Singer Y.: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research 12/2011, 2121–2159.
Gales M., Young S.: The Application of Hidden Markov Models in Speech Recognition. Foundations and Trends in Signal Processing 1(3)/2007, 195–304.
DOI: https://doi.org/10.1561/2000000004
Graves A., Fernandez S., Gomez F., Schmidhuber J.: Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, 2006.
DOI: https://doi.org/10.1145/1143844.1143891
Graves A., Jaitly N.: Towards End-to-End Speech Recognition with Recurrent Neural Networks. Proceedings of the 31st International Conference on Machine Learning 2014.
Kingma D.P., Ba J.: Adam: A Method For Stochastic Optimization. Proc. 3rd International Conference for Learning Representations. 2015 arXiv:1412.6980v9.
Loizou N., Richtarik P.: Momentum and Stochastic Momentum for Stochastic Gradient, Newton, Proximal Point and Subspace Descent Methods. 2017, arXiv:1712.09677v2
Mussabayev R.R., Amirgaliyev N., Tairova A.T., Mussabayev T.R., Koibagarov K.C.: The technology for the automatic formation of the personal digital voice pattern. Application of Information and Communication Technologies AICT 2016.
DOI: https://doi.org/10.1109/ICAICT.2016.7991733
Schuster M., Paliwal K.K.: Bidirectional recurrent neural networks. Signal Processing. IEEE Transactions 45(11)/1997, 2673–2681.
DOI: https://doi.org/10.1109/78.650093
Sutskever I., Vinyals O., Le Q.V.: Sequence to sequence learning with neural networks. Advances in Neural Information Processing Systems 2014, 3104–3112.
Wiseman S., Rush A.M.: Sequence-to-Sequence Learning as Beam-Search Optimization. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing 2016.
DOI: https://doi.org/10.18653/v1/D16-1137
Yu D., Li J.: Recent Progresses in Deep Learning based Acoustic Models. Tencent AI Lab, Microsoft AI and Research, 2018.
