Abstract
Hybrid speech recognition systems incorporating deep neural networks (DNNs) with Hidden Markov Models/Gaussian Mixture Models have achieved good results. We propose applying various DNNs in automatic recognition of Russian continuous speech. We used different neural network models such as Convolutional Neural Networks (CNNs), modifications of Long short-term memory (LSTM), Residual Networks and Recurrent Convolutional Networks (RCNNs). The presented model achieved \(7.5\%\) reducing of word error rate (WER) compared with Kaldi baseline. Experiments are performed with extra-large vocabulary (more than 30 h) of Russian speech.
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References
Benesty, J., Sondhi, M.M., Huang, Y.: Introduction to speech processing. In: Benesty, J., Sondhi, M.M., Huang, Y.A. (eds.) Springer Handbook of Speech Processing. Springer Handbooks, pp. 1–4. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-49127-9_1
Hinton, G., Deng, L., Yu, D., Dahl, G.E., Mohamed, A.R., Jaitly, N., Kingsbury, B.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)
Povey, D., Ghoshal, A., Boulianne, G., Burget, L., Glembek, O., Goel, N., Silovsky, J.: The Kaldi speech recognition toolkit. In: IEEE 2011 Workshop on Automatic Speech Recognition and Understanding (No. EPFŁ-CONF-192584). IEEE Signal Processing Society (2011)
Vesel K., Ghoshal, A., Burget, L., Povey, D.: Sequence-discriminative training of deep neural networks. In: Interspeech, pp. 2345–2349 (2013)
Povey, D., Zhang, X., Khudanpur, S.: Parallel training of DNNs with natural gradient and parameter averaging (2014). arXiv preprint arXiv:1410.7455
Cosi, P.: A KALDI-DNN-based ASR system for Italian. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–5. IEEE (2015)
Popović, B., Ostrogonac, S., Pakoci, E., Jakovljević, N., Delić, V.: Deep neural network based continuous speech recognition for Serbian using the Kaldi toolkit. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds.) SPECOM 2015. LNCS, vol. 9319, pp. 186–192. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23132-7_23
Prudnikov, A., Medennikov, I., Mendelev, V., Korenevsky, M., Khokhlov, Y.: Improving acoustic models for Russian spontaneous speech recognition. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds.) SPECOM 2015. LNCS, vol. 9319, pp. 234–242. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23132-7_29
Kipyatkova, I., Karpov, A.: DNN-based acoustic modeling for Russian speech recognition using Kaldi. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS, vol. 9811, pp. 246–253. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_29
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10. The MIT Press, Cambridge (1995)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Liang, M., Hu, X.: Recurrent convolutional neural network for object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3367–3375 (2015)
Liang, M., Hu, X., Zhang, B.: Convolutional neural networks with intra-layer recurrent connections for scene labeling. In: Advances in Neural Information Processing Systems, pp. 937–945. Morgan Kaufmann Publishers Inc., San Francisco (2015)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift (2015). arXiv preprint arXiv:1502.03167
Verkhodanova, V., Ronzhin, A., Kipyatkova, I., Ivanko, D., Karpov, A., Železný, M.: HAVRUS corpus: high-speed recordings of audio-visual Russian speech. In: Ronzhin, A., Potapova, R., Németh, G. (eds.) SPECOM 2016. LNCS, vol. 9811, pp. 338–345. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_40
Kipyatkova, I.S., Karpov, A.A.: Automatic processing and statistic analysis of a news text corpus for a language model of a Russian language speech recognition system. Inf. Upravl. Sist. 4(47), 28 (2010)
Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. In: Proceedings of the 34th Annual Meeting on Association for Computational Linguistics, pp. 310–318. Association for Computational Linguistics (1996)
Fox, J., Zou, Y., Qiu, J.: Software frameworks for deep learning at scale. Internal Indiana University Technical report (2016)
Kovalev, V., Kalinovsky, A., Kovalev, S.: Deep Learning with Theano, Torch, Caffe, Tensorflow, and Deeplearning4J: Which One is the Best in Speed and Accuracy? (2016)
Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., Zhang, Z.: Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems (2015). arXiv preprint arXiv:1512.01274
Bahrampour, S., Ramakrishnan, N., Schott, L., Shah, M.: Comparative study of deep learning software frameworks (2015). arXiv preprint arXiv:1511.06435
Ganchev, T., Fakotakis, N., Kokkinakis, G.: Comparative evaluation of various MFCC implementations on the speaker verification task. In: Proceedings of the SPECOM, vol. 1, pp. 191–194 (2005)
Gopinath, R.A.: Maximum likelihood modeling with Gaussian distributions for classification. In: Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 2, pp. 661–664. IEEE (1998)
Anastasakos, T., McDonough, J., Makhoul, J.: Speaker adaptive training: a maximum likelihood approach to speaker normalization. In: 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1997, vol. 2, pp. 1043–1046. IEEE (1997)
Gales, M.J.: Maximum likelihood linear transformations for HMM-based speech recognition. Comput. Speech Lang. 12(2), 75–98 (1998)
Ackley, D.H., Hinton, G.E., Sejnowski, T.J.: A learning algorithm for Boltzmann machines. Cogn. Sci. 9(1), 147–169 (1985)
Acknowledgments
This research is partially supported by the Council for Grants of the President of the Russian Federation (project No. MK-1000.2017.8) and by the Russian Foundation for Basic Research (project No. 15-07-04322).
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Markovnikov, N., Kipyatkova, I., Karpov, A., Filchenkov, A. (2018). Deep Neural Networks in Russian Speech Recognition. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds) Artificial Intelligence and Natural Language. AINL 2017. Communications in Computer and Information Science, vol 789. Springer, Cham. https://doi.org/10.1007/978-3-319-71746-3_5
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