| Autor: |
Nurlankyzy, Aigul, Akhmediyarova, Ainur, Zhetpisbayeva, Ainur, Namazbayev, Timur, Yskak, Asset, Yerzhan, Nurdaulet, Medetov, Bekbolat |
| Předmět: |
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| Zdroj: |
Eastern-European Journal of Enterprise Technologies; 2024, Vol. 127 Issue 9, p72-81, 10p |
| Abstrakt: |
This study examines the effectiveness of neural network architectures (multilayer perceptron MLP, convolutional neural network CNN, recurrent neural network RNN) for human voice recognition, with an emphasis on the Kazakh language. Problems related to language, the difference between speakers, and the influence of network architecture on recognition accuracy are considered. The methodology includes extensive training and testing, studying the accuracy of recognition in different languages, and different sets of data on speakers. Using a comparative analysis, this study evaluates the performance of three architectures trained exclusively in the Kazakh language. The testing included statements in Kazakhs and other languages, while the number of speakers varied to assess its impact on recognition accuracy. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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