Abstract
Modern deep learning-based speech recognition methods allow for achieving phenomenal speech recognition accuracy. But this requires enormous amounts of data to train. Unfortunately, developers of recognizers for less widely spoken languages are often facing the problem of scarce resources to train recognizers. The paper presents a novel method to increase recognition accuracy by post-processing of the text outputs of two different speech recognizers. The method is using machine learning to find a more likely symbol or group of symbols from two different deep learning-based recognizers. The experiments showed that the method allows increasing recognition accuracy by 3%.
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References
Bourlard, H., Morgan, N.: Connectionist Speech Recognition: A Hybrid Approach. Kluwer Press, Amsterdam (1994)
Tebelskis, J.: Speech recognition using neural networks. Ph.D. thesis, CMU (1995)
Hinton, G., Osindero, S., Yee-Wee, T.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
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)
Saon, G., Chien, J.: Large-vocabulary continuous speech recognition systems: a look at some recent advances. Signal Process. Mag. 29(6), 18–33 (2012)
Graves, A., Jaitly, N., Mohamed, A.-R.: Hybrid speech recognition with deep bidirectional LSTM. In: Poroceedings of 2013 IEEE Workshop on Automatic Speech Recognition and Understanding, Olomouc, Czech Republic, pp. 273–278 (2013)
Hannun, A., et al.: Deep speech: scaling up end-to-end speech recognition (2014). https://arxiv.org/abs/1412.5567
Amodei, D., et al.: Deep speech 2: end-to-end speech recognition in English and mandarin. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, pp. 173–182 (2016)
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Rudzionis, V., Malukas, U., Danieliene, R. (2022). Improvement of Speech Recognition Accuracy Using Post-processing of Recognized Text. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2022. Communications in Computer and Information Science, vol 1665. Springer, Cham. https://doi.org/10.1007/978-3-031-16302-9_21
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DOI: https://doi.org/10.1007/978-3-031-16302-9_21
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