SPEECH COMMANDS RECOGNITION FOR UZBEK LANGUAGE USING AUDIO SPECTROGRAM TRANSFORMER (DIGITAL TRANSFORMATION AND ARTIFICIAL INTELLIGENCE, 2024)
This paper presents a novel approach for training speech recognition models for the Uzbek language using the Audio Spectrogram Transformer (AST) algorithm. The study involves the collection of a dataset comprising speech commands in Uzbek, recorded with 28 speakers, and the training of AST models using this dataset. Two experiments are conducted: the first involves training AST models exclusively on the Uzbek dataset, while the second combines the Uzbek dataset with the widely used speechcommand v2 dataset. The results demonstrate the effectiveness of the AST algorithm in accurately recognizing Uzbek speech commands, with the combined dataset yielding particularly promising results. The evaluation accuracy for the combined dataset experiment reached 97.96%. The trained models are integrated into a Telegram bot (@UzVoiceDataSetBot), enabling users to interact with speech commands in Uzbek with high accuracy and speed. This research contributes to the advancement of speech recognition technology for the Uzbek language and highlights the potential of AST in multilingual speech processing applications.
Keywords: Speech Recognition; Uzbek Language; Audio Spectrogram Transformer; AST; Dataset Collection; Telegram Bot