Abstract
This article focuses on the problems that arise in the recognition of speech through machine learning and the methods based on in-depth learning used to overcome them, which outlines approaches to the transition to a coding-decoding architecture system based on the attention mechanism. It also describes the hybrid CTC/Attention architecture, which is now widely used in speech recognition. In recent years, models of neural network architectures and neural network model based on attention mechanism, which are widely used in automatic speech recognition, have been proposed, which are taught on the basis of Uzbek and Russian speech corpuscles and the results obtained are comparatively analyzed.
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Mamatov, N.S., Niyozmatova, N.A., Yuldoshev, Y.S., Abdullaev, S.S., Samijonov, A.N. (2023). Automatic Speech Recognition on the Neutral Network Based on Attention Mechanism. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_11
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