Cargando…
A study of transformer-based end-to-end speech recognition system for Kazakh language
Today, the Transformer model, which allows parallelization and also has its own internal attention, has been widely used in the field of speech recognition. The great advantage of this architecture is the fast learning speed, and the lack of sequential operation, as with recurrent neural networks. I...
Autores principales: | Orken, Mamyrbayev, Dina, Oralbekova, Keylan, Alimhan, Tolganay, Turdalykyzy, Mohamed, Othman |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9117202/ https://www.ncbi.nlm.nih.gov/pubmed/35585130 http://dx.doi.org/10.1038/s41598-022-12260-y |
Ejemplares similares
-
A Study of Speech Recognition for Kazakh Based on Unsupervised Pre-Training
por: Meng, Weijing, et al.
Publicado: (2023) -
Advances in Completely Automated Vowel Analysis for Sociophonetics: Using End-to-End Speech Recognition Systems With DARLA
por: Coto-Solano, Rolando, et al.
Publicado: (2021) -
End-to-end emotional speech recognition using acoustic model adaptation based on knowledge distillation
por: Yun, Hong-In, et al.
Publicado: (2023) -
Dynamic Acoustic Unit Augmentation with BPE-Dropout for Low-Resource End-to-End Speech Recognition
por: Laptev, Aleksandr, et al.
Publicado: (2021) -
End-to-end speech emotion recognition using a novel context-stacking dilated convolution neural network
por: Tang, Duowei, et al.
Publicado: (2021)