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Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case

Author

Listed:
  • Irina Kipyatkova

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia)

  • Ildar Kagirov

    (St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178 St. Petersburg, Russia)

Abstract

Recently, there has been a growth in the number of studies addressing the automatic processing of low-resource languages. The lack of speech and text data significantly hinders the development of speech technologies for such languages. This paper introduces an automatic speech recognition system for Livvi-Karelian. Acoustic models based on artificial neural networks with time delays and hidden Markov models were trained using a limited speech dataset of 3.5 h. To augment the data, pitch and speech rate perturbation, SpecAugment, and their combinations were employed. Language models based on 3-grams and neural networks were trained using written texts and transcripts. The achieved word error rate metric of 22.80% is comparable to other low-resource languages. To the best of our knowledge, this is the first speech recognition system for Livvi-Karelian. The results obtained can be of a certain significance for development of automatic speech recognition systems not only for Livvi-Karelian, but also for other low-resource languages, including the fields of speech recognition and machine translation systems. Future work includes experiments with Karelian data using techniques such as transfer learning and DNN language models.

Suggested Citation

  • Irina Kipyatkova & Ildar Kagirov, 2023. "Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case," Mathematics, MDPI, vol. 11(18), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3814-:d:1233512
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    References listed on IDEAS

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    1. Denis Ivanko & Dmitry Ryumin & Alexey Karpov, 2023. "A Review of Recent Advances on Deep Learning Methods for Audio-Visual Speech Recognition," Mathematics, MDPI, vol. 11(12), pages 1-30, June.
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