Speech Recognition of Punjabi Numerals Using Convolutional Neural Networks

2018 ◽  
pp. 61-69
Author(s):  
Thakur Aditi ◽  
Verma Karun
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Convolutional Neural Networks

scholarly journals Convolutional Neural Networks for Distant Speech Recognition

2014 ◽  
Vol 21 (9) ◽  
pp. 1120-1124 ◽  
Author(s):  
Pawel Swietojanski ◽  
Arnab Ghoshal ◽  
Steve Renals
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Convolutional Neural Networks

Efficient GPU implementation of convolutional neural networks for speech recognition

2015 ◽  
Author(s):  
Ewout van den Berg ◽  
Daniel Brand ◽  
Rajesh Bordawekar ◽  
Leonid Rachevsky ◽  
Bhuvana Ramabhadran
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Convolutional Neural Networks ◽  
Gpu Implementation

scholarly journals Lipreading Architecture Based on Multiple Convolutional Neural Networks for Sentence-Level Visual Speech Recognition

Sensors ◽  
2021 ◽  
Vol 22 (1) ◽  
pp. 72
Author(s):  
Sanghun Jeon ◽  
Ahmed Elsharkawy ◽  
Mun Sang Kim
Keyword(s):  
Neural Networks ◽  
Speech Recognition ◽  
Convolutional Neural Networks ◽  
Visual Information ◽  
Feature Fusion ◽  
Error Rates ◽  
Visual Speech ◽  
Technical Limitation ◽  
Visual Speech Recognition ◽  
3D Cnn

In visual speech recognition (VSR), speech is transcribed using only visual information to interpret tongue and teeth movements. Recently, deep learning has shown outstanding performance in VSR, with accuracy exceeding that of lipreaders on benchmark datasets. However, several problems still exist when using VSR systems. A major challenge is the distinction of words with similar pronunciation, called homophones; these lead to word ambiguity. Another technical limitation of traditional VSR systems is that visual information does not provide sufficient data for learning words such as “a”, “an”, “eight”, and “bin” because their lengths are shorter than 0.02 s. This report proposes a novel lipreading architecture that combines three different convolutional neural networks (CNNs; a 3D CNN, a densely connected 3D CNN, and a multi-layer feature fusion 3D CNN), which are followed by a two-layer bi-directional gated recurrent unit. The entire network was trained using connectionist temporal classification. The results of the standard automatic speech recognition evaluation metrics show that the proposed architecture reduced the character and word error rates of the baseline model by 5.681% and 11.282%, respectively, for the unseen-speaker dataset. Our proposed architecture exhibits improved performance even when visual ambiguity arises, thereby increasing VSR reliability for practical applications.

scholarly journals Assistant robot through deep learning

2020 ◽  
Vol 10 (1) ◽  
pp. 1053
Author(s):  
Robinson Jiménez-Moreno ◽  
Javier Orlando Pinzón-Arenas ◽  
César Giovany Pachón-Suescún
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Speech Recognition ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Spanish Language ◽  
Assistive Robotics ◽  
The One

This article presents a work oriented to assistive robotics, where a scenario is established for a robot to reach a tool in the hand of a user, when they have verbally requested it by his name. For this, three convolutional neural networks are trained, one for recognition of a group of tools, which obtained an accuracy of 98% identifying the tools established for the application, that are scalpel, screwdriver and scissors; one for speech recognition, trained with the names of the tools in Spanish language, where its validation accuracy reach a 97.5% in the recognition of the words; and another for recognition of the user's hand, taking in consideration the classification of 2 gestures: Open and Closed hand, where a 96.25% accuracy was achieved. With those networks, tests in real time are performed, presenting results in the delivery of each tool with a 100% of accuracy, i.e. the robot was able to identify correctly what the user requested, recognize correctly each tool and deliver the one need when the user opened their hand, taking an average time of 45 seconds in the execution of the application.

Sign in / Sign up

Export Citation Format

Share Document