Hybrid end-to-end model for Kazakh speech recognition

• Published in: International journal of speech technology Vol. 26; no. 2; pp. 261 - 270
• Main Authors: Mamyrbayev, Orken Zh, Oralbekova, Dina O., Alimhan, Keylan, Nuranbayeva, Bulbul M.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Automatic speech recognition; Corpus linguistics; Engineering; Kazakh language; Language; Language modeling; Performance enhancement; Signal,Image and Speech Processing; Social Sciences; Speech; Speech recognition; Training; Voice recognition; Connectionist temporal classification; End-to-end; Attention; Low resource language; Automatic speech recognition

Modern automatic speech recognition systems based on end-to-end (E2E) models show good results in terms of the accuracy of language recognition, which have large corpuses for several thousand hours of speech for system training. Such models require a very large amount of training data, which is prob...