End-to-end neural automatic speech recognition system for low resource languages
The rising popularity of end-to-end (E2E) automatic speech recognition (ASR) systems can be attributed to their ability to learn complex speech patterns directly from raw data, eliminating the need for intricate feature extraction pipelines and handcrafted language models. E2E-ASR systems have consi...
Saved in:
Main Authors: | Sami Dhahbi, Nasir Saleem, Sami Bourouis, Mouhebeddine Berrima, Elena Verdú |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-03-01
|
Series: | Egyptian Informatics Journal |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S1110866525000088 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
CNN Based Automatic Speech Recognition: A Comparative Study
by: Hilal Ilgaz, et al.
Published: (2024-08-01) -
WTASR: Wavelet Transformer for Automatic Speech Recognition of Indian Languages
by: Tripti Choudhary, et al.
Published: (2023-03-01) -
A review on speech recognition approaches and challenges for Portuguese: exploring the feasibility of fine-tuning large-scale end-to-end models
by: Yan Li, et al.
Published: (2025-01-01) -
Speech Recognition System Based on Machine Learning in Persian Language
by: Shahed Mohammadi, et al.
Published: (2022-06-01) -
An End-To-End Speech Recognition Model for the North Shaanxi Dialect: Design and Evaluation
by: Yi Qin, et al.
Published: (2025-01-01)