Development of Automatic Speech Recognition for Kazakh Language using Transfer Learning

Development of an automatic speech recognition system for the Kazakh language is a challenging task due to the lack of audio data and specificity and complexity of the language itself. In this paper, we propose a new method which gets a pre-trained model of the russian language and uses the weight values of the pre-trained model in the proposed neural network. The main reason for choosing the Russian language model is that the pronunciation of the Kazakh and Russian languages is very similar in many respects, because they account for 78% of the total letters and there is a rather large corpus of the Russian speech dataset. The dataset of Kazakh speech with transcriptions was formed by the university's faculty. In general, 50 native speakers were involved who generated about 400 sentences. A special technology has been created for the automatic expansion of the database. The data was extracted from well-known Kazakh books such as "Abai zholy", "Kara sozder", etc.