Идентификация языка устной речи с использованием модели Wav2Vec2 для казахского языка
Year: 2025
Type: article
Abstract: This study presents the development and fine-tuning of an oral language identification model using the XLSR (Cross-Lingual Speech Recognition) Wav2Vec2 variant. Trained on a rich and diverse dataset spanning six languages, with a particular focus on low-resource languages such as Kazakh, the model demonstrates remarkable capabilities in multilingual speech recognition. Thanks to extensive evaluation, the finely tuned model not only surpasses existing benchmarks, but also surpasses other modern models, including Whisper variants. Having achieved an impressive F1 score of 92.9% and an accuracy of 93%, the model demonstrates its performance in real multilingual and low-resource scenarios. This work makes a significant contribution to the development of speech recognition technologies by providing a reliable solution for language identification in various language environments, especially in underrepresented language settings. Its success highlights the potential of Wav2Vec2-based models in improving speech processing systems in low-resource multilingual contexts. The results of this analysis can contribute to the development of reliable and effective automatic speech recognition systems optimized for the Kazakh language. Such technologies will find applications in various fields, including speech-to-text conversion, voice assistants and voice communication tools.
Authors: Kozhirbayev , Zh., Umbet , S.
Institutions: Nazarbayev University, Universität Trier
Language: Russian
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7220536568