Review of methods of end-to-end automatic recognition of Kazakh speech

Abstract

This paper provides a comprehensive review of end-to-end automatic speech recognition methods for the Kazakh language, which is considered a low-resource language with unique phonetic and grammatical features. These features present significant challenges for automatic speech recognition systems. The review, conducted in accordance with the Arksey and O'Malley framework, evaluated the effectiveness of various approaches, including traditional methods that involve separate stages of acoustic modeling, language modeling, and lexicon development, as well as modern end-to-end models. Our analysis identified 22 relevant studies published between 2019 and 2024, including 8 controlled trials, 2 design studies, and 12 intervention studies. The findings demonstrate that end-to-end models, such as Connectionist Temporal Classification (CTC) and Recurrent Neural Network Transducer (RNN-T), show promise in improving the accuracy of Kazakh speech recognition by integrating all recognition stages into a single neural network. However, challenges remain, particularly in the availability of annotated data and the need for more robust models tailored to the Kazakh language.

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