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End-to-End Speech Recognition in Russian

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Speech and Computer (SPECOM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11096))

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Abstract

End-to-end speech recognition systems incorporating deep neural networks (DNNs) have achieved good results. We propose applying CTC (Connectionist Temporal Classification) models and attention-based encoder-decoder in automatic recognition of the Russian continuous speech. We used different neural network models such Long short-term memory (LSTM), bidirectional LSTM and Residual Networks to provide experiments. We got recognition accuracy a bit worse than hybrid models but our models can work without large language model and they showed better performance in terms of average decoding speed that can be helpful in real systems. Experiments are performed with extra-large vocabulary (more than 150K words) of Russian speech.

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Notes

  1. 1.

    http://www.fontanka.ru/.

  2. 2.

    https://docs.microsoft.com/ru-ru/cognitive-toolkit/.

  3. 3.

    https://www.tensorflow.org/.

  4. 4.

    https://github.com/mikita95/asr-e2e.

  5. 5.

    https://github.com/tensorflow/tensor2tensor.

References

  1. Allauzen, C., Riley, M., Schalkwyk, J., Skut, W., Mohri, M.: OpenFst: a general and efficient weighted finite-state transducer library. In: Implementation and Application of Automata, pp. 11–23 (2007)

    Google Scholar 

  2. Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. CoRR abs/1409.0473 (2014). http://arxiv.org/abs/1409.0473

  3. Bahdanau, D., Chorowski, J., Serdyuk, D., Brakel, P., Bengio, Y.: End-to-end attention-based large vocabulary speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4945–4949. IEEE (2016)

    Google Scholar 

  4. Chan, W., Jaitly, N., Le, Q., Vinyals, O.: Listen, attend and spell: a neural network for large vocabulary conversational speech recognition. In: 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4960–4964. IEEE (2016)

    Google Scholar 

  5. Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. Comput. Speech Lang. 13(4), 359–394 (1999). https://doi.org/10.1006/csla.1999.0128. http://www.sciencedirect.com/science/article/pii/S0885230899901286

    Article  Google Scholar 

  6. Cho, K., van Merrienboer, B., Gülçehre, Ç., Bougares, F., Schwenk, H., Bengio, Y.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. CoRR abs/1406.1078 (2014). http://arxiv.org/abs/1406.1078

  7. Chorowski, J.K., Bahdanau, D., Serdyuk, D., Cho, K., Bengio, Y.: Attention-based models for speech recognition. In: Advances in Neural Information Processing Systems, pp. 577–585 (2015)

    Google Scholar 

  8. Ganchev, T., Fakotakis, N., Kokkinakis, G.: Comparative evaluation of various MFCC implementations on the speaker verification task. Proc. SPECOM 1, 191–194 (2005)

    Google Scholar 

  9. Graves, A., Fernández, S., Gomez, F., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the 23rd international conference on Machine learning, pp. 369–376. ACM (2006)

    Google Scholar 

  10. Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1764–1772 (2014)

    Google Scholar 

  11. 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)

    Google Scholar 

  12. Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)

    Article  Google Scholar 

  13. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. CoRR abs/1502.03167 (2015). http://arxiv.org/abs/1502.03167

  14. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014). http://arxiv.org/abs/1412.6980

  15. Kipyatkova, I., Karpov, A.: Lexicon size and language model order optimization for Russian LVCSR. In: Železný, M., Habernal, I., Ronzhin, A. (eds.) Speech and Computer, pp. 219–226. Springer, Cham (2013)

    Chapter  Google Scholar 

  16. 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 (LNAI), vol. 9811, pp. 246–253. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_29

    Chapter  Google Scholar 

  17. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw. 3361(10), 1995 (1995)

    Google Scholar 

  18. Liang, M., Hu, X.: Recurrent convolutional neural network for object recognition. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3367–3375, June 2015. https://doi.org/10.1109/CVPR.2015.7298958

  19. Liao, H., McDermott, E., Senior, A.: Large scale deep neural network acoustic modeling with semi-supervised training data for YouTube video transcription. In: 2013 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 368–373. IEEE (2013)

    Google Scholar 

  20. Markovnikov, N., Kipyatkova, I., Karpov, A., Filchenkov, A.: Deep neural networks in Russian speech recognition. In: Filchenkov, A., Pivovarova, L., Žižka, J. (eds.) AINL 2017. CCIS, vol. 789, pp. 54–67. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-71746-3_5

    Chapter  Google Scholar 

  21. Miao, Y., Gowayyed, M., Metze, F.: EESEN: End-to-end speech recognition using deep RNN models and WFST-based decoding. In: 2015 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), pp. 167–174. IEEE (2015)

    Google Scholar 

  22. Mohri, M., Pereira, F., Riley, M.: Weighted finite-state transducers in speech recognition. Comput. Speech Lang. 16(1), 69–88 (2002)

    Article  Google Scholar 

  23. Popović, B., Pakoci, E., Pekar, D.: End-to-End large vocabulary speech recognition for the Serbian language. In: Karpov, A., Potapova, R., Mporas, I. (eds.) SPECOM 2017. LNCS (LNAI), vol. 10458, pp. 343–352. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66429-3_33

    Chapter  Google Scholar 

  24. Povey, D., et al.: The kaldi speech recognition toolkit. In: IEEE 2011 workshop on automatic speech recognition and understanding, No. EPFL-CONF-192584, IEEE Signal Processing Society (2011)

    Google Scholar 

  25. Ravindran, S., Demirogulu, C., Anderson, D.V.: Speech recognition using filter-bank features. In: The Thrity-Seventh Asilomar Conference on Signals, Systems Computers, vol. 2, pp. 1900–1903, November 2003. https://doi.org/10.1109/ACSSC.2003.1292312

  26. Soltau, H., Liao, H., Sak, H.: Neural speech recognizer: Acoustic-to-word LSTM model for large vocabulary speech recognition (2016)

    Google Scholar 

  27. Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. In: Advances in neural information processing systems, pp. 3104–3112 (2014)

    Google Scholar 

  28. 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 (LNAI), vol. 9811, pp. 338–345. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-43958-7_40

    Chapter  Google Scholar 

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Acknowledgments

This research is supported by the Russian Science Foundation (project No. 18-11-00145).

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Authors and Affiliations

  1. St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS), Saint-Petersburg, Russia

    Nikita Markovnikov, Irina Kipyatkova & Elena Lyakso

Authors
  1. Nikita Markovnikov
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  2. Irina Kipyatkova
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  3. Elena Lyakso
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Corresponding author

Correspondence to Nikita Markovnikov .

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Editors and Affiliations

  1. SPIIRAS, St. Petersburg, Russia

    Alexey Karpov

  2. Leipzig University of Telecommunications, Leipzig, Germany

    Oliver Jokisch

  3. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Markovnikov, N., Kipyatkova, I., Lyakso, E. (2018). End-to-End Speech Recognition in Russian. In: Karpov, A., Jokisch, O., Potapova, R. (eds) Speech and Computer. SPECOM 2018. Lecture Notes in Computer Science(), vol 11096. Springer, Cham. https://doi.org/10.1007/978-3-319-99579-3_40

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  • DOI: https://doi.org/10.1007/978-3-319-99579-3_40

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99578-6

  • Online ISBN: 978-3-319-99579-3

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