Year: 2023
Type: conference-paper
Abstract: Nowadays, speech-processing technologies with different language systems are successfully used in mobile and stationary devices. Kazakh is considered a low-resource language, which poses various challenges for conventional speech recognition methods. This paper presents a proposed model capable of multitasking and handling concurrent speech recognition, dialect identification, and speaker identification, all in an end-to-end framework. The developed multitask model enables training three different tasks within a single model. A multitask recognition system is created based on the WaveNet-CTC model. Experiments show that for the concrete task end-to-end multitask model has better performance than other models.
DOI: 10.1117/12.3022727
Institutions: Institute of Information and Computational Technologies, Vinnytsia National Technical University, Satbayev University, Narxoz University, Almaty University of Power Engineering and Telecommunications, Institute of Economics, Vinnytsia Mykhailo Kotsiubynskyi State Pedagogical University, Lublin University of Technology, Turan University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4389991520