Application of the conformer model for kazakh speech recognition
Year: 2024
Type: article
Abstract: The article describes the application of a transformer-based conformer model and a convolutional neural network (CNN) in Kazakh speech recognition, with an overview of automatic speech recognition (ASR) technologies. By exploring ways to combine convolutional neural networks and transformers, the best aspects of both models were combined to simulate audio circuits. The structure of the Conformer model is described. To determine the performance of the Conformer model, it was compared with previous Transformer and CNN models. According to the results of the study, the Conformer model outperformed the Transformer and CNN models in recognizing Kazakh speech, and we achieved higher accuracy than our previous studies. During testing, the word error rate (WER) dropped to 12.3 and the letter error rate (SER) dropped to 6.9 percent.
Authors: O. Mamyrbayev, T. Kurmetkan, R. S. Arslan
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4403310207