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USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments

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Speech and Computer (SPECOM 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12997))

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Abstract

We present a freely available speech corpus for the Uzbek language and report preliminary automatic speech recognition (ASR) results using both the deep neural network hidden Markov model (DNN-HMM) and end-to-end (E2E) architectures. The Uzbek speech corpus (USC) comprises 958 different speakers with a total of 105 h of transcribed audio recordings. To the best of our knowledge, this is the first open-source Uzbek speech corpus dedicated to the ASR task. To ensure high quality, the USC has been manually checked by native speakers. We first describe the design and development procedures of the USC, and then explain the conducted ASR experiments in detail. The experimental results demonstrate promising results for the applicability of the USC for ASR. Specifically, 18.1% and 17.4% word error rates were achieved on the validation and test sets, respectively. To enable experiment reproducibility, we share the USC dataset, pre-trained models, and training recipes in our GitHub repository (https://github.com/IS2AI/Uzbek_ASR).

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Notes

  1. 1.

    https://creativecommons.org/licenses/by/4.0/.

  2. 2.

    https://github.com/IS2AI/Uzbek_ASR.

  3. 3.

    We trained several N-gram LMs with different orders and smoothing techniques and picked the one that obtained the best perplexity score on the validation set.

  4. 4.

    Note that the Uzbek Latin alphabet contains 29 letters, however, some of the letters are represented using digraphs (e.g., ng, sh, ch, o’ and g’), which we broke down into smaller units and obtained 25 letters. The 26th letter is ‘w’ obtained from international words.

References

  1. Speechocean’s Uzbek speech corpus. http://en.speechocean.com/datacenter/details/1847.html. Accessed 21 May 2021

  2. Uzbek language. https://en.wikipedia.org/wiki/Uzbek_language. Accessed 20 May 2021

  3. Voxforge. http://www.voxforge.org/. Accessed 11 May 2021

  4. Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82–97 (2012)

    Article  Google Scholar 

  5. Abdel-Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE ACM Trans. Audio Speech Lang. Process. 22(10), 1533–1545 (2014)

    Google Scholar 

  6. Ardila, R., et al.: Common voice: a massively-multilingual speech corpus. In: LREC, pp. 4218–4222. ELRA (2020)

    Google Scholar 

  7. Besacier, L., Barnard, E., Karpov, A., Schultz, T.: Automatic speech recognition for under-resourced languages: a survey. Speech Commun. 56, 85–100 (2014)

    Article  Google Scholar 

  8. Bu, H., Du, J., Na, X., Wu, B., Zheng, H.: AISHELL-1: an open-source Mandarin speech corpus and a speech recognition baseline. In: Proceedings of the O-COCOSDA, pp. 1–5. IEEE (2017)

    Google Scholar 

  9. Doumbouya, M., Einstein, L., Piech, C.: Using radio archives for low-resource speech recognition: towards an intelligent virtual assistant for illiterate users. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 14757–14765. AAAI Press (2021)

    Google Scholar 

  10. Gales, M.J.F., Knill, K.M., Ragni, A., Rath, S.P.: Speech recognition and keyword spotting for low-resource languages: Babel project research at CUED. In: Proceedings of the Workshop on Spoken Language Technologies for Under-Resourced Languages (SLTU), pp. 16–23. ISCA (2014)

    Google Scholar 

  11. Graves, A., Fernández, S., Gomez, F.J., Schmidhuber, J.: Connectionist temporal classification: labelling unsegmented sequence data with recurrent neural networks. In: Proceedings of the International Conference on Machine Learning (ICML), Pittsburgh, Pennsylvania, USA, 25–29 June 2006, pp. 369–376 (2006)

    Google Scholar 

  12. Graves, A., Jaitly, N.: Towards end-to-end speech recognition with recurrent neural networks. In: Proceedings of the International Conference on Machine Learning (ICML), Beijing, China, 21–26 June 2014, vol. 32, pp. 1764–1772 (2014)

    Google Scholar 

  13. Graves, A., Mohamed, A., Hinton, G.E.: Speech recognition with deep recurrent neural networks. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 6645–6649. IEEE (2013)

    Google Scholar 

  14. Gulati, A., et al.: Conformer: convolution-augmented transformer for speech recognition. In: Proceedings of INTERSPEECH, pp. 5036–5040. ISCA (2020)

    Google Scholar 

  15. Gülçehre, Ç., et al.: On using monolingual corpora in neural machine translation. CoRR abs/1503.03535 (2015). http://arxiv.org/abs/1503.03535

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

    Article  Google Scholar 

  17. Khassanov, Y., Mussakhojayeva, S., Mirzakhmetov, A., Adiyev, A., Nurpeiissov, M., Varol, H.A.: A crowdsourced open-source Kazakh speech corpus and initial speech recognition baseline. In: Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics, pp. 697–706. Association for Computational Linguistics, April 2021

    Google Scholar 

  18. Kim, S., Hori, T., Watanabe, S.: Joint CTC-attention based end-to-end speech recognition using multi-task learning. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4835–4839 (2017)

    Google Scholar 

  19. Ko, T., Peddinti, V., Povey, D., Khudanpur, S.: Audio augmentation for speech recognition. In: Proceedings of INTERSPEECH, pp. 3586–3589 (2015)

    Google Scholar 

  20. Musaev, M., Khujayorov, I., Ochilov, M.: Image approach to speech recognition on CNN. In: Proceedings of the International Symposium on Computer Science and Intelligent Control (ISCSIC), pp. 57:1–57:6. ACM (2019)

    Google Scholar 

  21. Musaev, M., Khujayorov, I., Ochilov, M.: Development of integral model of speech recognition system for Uzbek language. In: Proceedings of the IEEE International Conference on Application of Information and Communication Technologies (AICT), pp. 1–6. IEEE (2020)

    Google Scholar 

  22. Musaev, M., Khujayorov, I., Ochilov, M.: The use of neural networks to improve the recognition accuracy of explosive and unvoiced phonemes in Uzbek language. In: Proceedings of the Information Communication Technologies Conference (ICTC), pp. 231–234. IEEE (2020)

    Google Scholar 

  23. Musaev, M., Khujayorov, I., Ochilov, M.: Automatic recognition of Uzbek speech based on integrated neural networks. In: Aliev, R.A., Yusupbekov, N.R., Kacprzyk, J., Pedrycz, W., Sadikoglu, F.M. (eds.) WCIS 2020. AISC, vol. 1323, pp. 215–223. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-68004-6_28

    Chapter  Google Scholar 

  24. Mussakhojayeva, S., Janaliyeva, A., Mirzakhmetov, A., Khassanov, Y., Varol, H.A.: KazakhTTS: an open-source Kazakh text-to-speech synthesis dataset. CoRR abs/2104.08459 (2021). https://arxiv.org/abs/2104.08459

  25. Panayotov, V., Chen, G., Povey, D., Khudanpur, S.: Librispeech: an ASR corpus based on public domain audio books. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 5206–5210. IEEE (2015)

    Google Scholar 

  26. Park, D.S., et al.: SpecAugment: a simple data augmentation method for automatic speech recognition. In: Proceedings of INTERSPEECH, pp. 2613–2617 (2019)

    Google Scholar 

  27. Povey, D., et al.: Semi-orthogonal low-rank matrix factorization for deep neural networks. In: Proceedings of INTERSPEECH, pp. 3743–3747 (2018)

    Google Scholar 

  28. Povey, D., et al.: The Kaldi speech recognition toolkit. In: Proceedings of the IEEE Workshop on Automatic Speech Recognition and Understanding (2011)

    Google Scholar 

  29. Povey, D., et al.: Purely sequence-trained neural networks for ASR based on lattice-free MMI. In: Proceedings of INTERSPEECH, pp. 2751–2755 (2016)

    Google Scholar 

  30. ReadBeyond: Aeneas. https://www.readbeyond.it/aeneas/

  31. Hernandez, F., Nguyen, V., Ghannay, S., Tomashenko, N., Estève, Y.: TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation. In: Karpov, A., Jokisch, O., Potapova, R. (eds.) SPECOM 2018. LNCS (LNAI), vol. 11096, pp. 198–208. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99579-3_21

    Chapter  Google Scholar 

  32. Solak, I.: The M-AILABS speech dataset. https://www.caito.de/2019/01/the-m-ailabs-speech-dataset/. Accessed 11 May 2021

  33. Telegram FZ LLC and Telegram Messenger Inc.: Telegram. https://telegram.org

  34. Vaswani, A., et al.: Attention is all you need. In: Proceedings of the Annual Conference on Neural Information Processing Systems, 4–9 December 2017, Long Beach, CA, USA, pp. 5998–6008 (2017)

    Google Scholar 

  35. Watanabe, S., et al.: ESPnet: end-to-end speech processing toolkit. In: Proceedings of INTERSPEECH, Hyderabad, India, 2–6 September 2018, pp. 2207–2211 (2018)

    Google Scholar 

  36. Yu, D., Deng, L.: Automatic Speech Recognition. SCT, Springer, London (2015). https://doi.org/10.1007/978-1-4471-5779-3

    Book  MATH  Google Scholar 

  37. Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701 (2012). http://arxiv.org/abs/1212.5701

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Authors and Affiliations

  1. Computer Systems, Tashkent University of Information Technology named after Muhammad Al-Khwarizmi, Tashkent, Uzbekistan

    Muhammadjon Musaev, Ilyos Khujayorov & Mannon Ochilov

  2. Institute of Smart Systems and Artificial Intelligence (ISSAI), Nazarbayev University, Nur-Sultan, Kazakhstan

    Saida Mussakhojayeva, Yerbolat Khassanov & Huseyin Atakan Varol

Authors
  1. Muhammadjon Musaev
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  2. Saida Mussakhojayeva
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  3. Ilyos Khujayorov
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  4. Yerbolat Khassanov
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  5. Mannon Ochilov
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  6. Huseyin Atakan Varol
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Corresponding author

Correspondence to Huseyin Atakan Varol .

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Editors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences, St. Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

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Musaev, M., Mussakhojayeva, S., Khujayorov, I., Khassanov, Y., Ochilov, M., Atakan Varol, H. (2021). USC: An Open-Source Uzbek Speech Corpus and Initial Speech Recognition Experiments. In: Karpov, A., Potapova, R. (eds) Speech and Computer. SPECOM 2021. Lecture Notes in Computer Science(), vol 12997. Springer, Cham. https://doi.org/10.1007/978-3-030-87802-3_40

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  • DOI: https://doi.org/10.1007/978-3-030-87802-3_40

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