Research Article
Nepali Speech Recognition using RNN-CTC Model
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 178 - Issue 31 |
| Published: Jul 2019 |
| Authors: Paribesh Regmi, Arjun Dahal, Basanta Joshi |
10.5120/ijca2019918401
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Paribesh Regmi, Arjun Dahal, Basanta Joshi . Nepali Speech Recognition using RNN-CTC Model. International Journal of Computer Applications. 178, 31 (Jul 2019), 1-6. DOI=10.5120/ijca2019918401
@article{ 10.5120/ijca2019918401,
author = { Paribesh Regmi,Arjun Dahal,Basanta Joshi },
title = { Nepali Speech Recognition using RNN-CTC Model },
journal = { International Journal of Computer Applications },
year = { 2019 },
volume = { 178 },
number = { 31 },
pages = { 1-6 },
doi = { 10.5120/ijca2019918401 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2019
%A Paribesh Regmi
%A Arjun Dahal
%A Basanta Joshi
%T Nepali Speech Recognition using RNN-CTC Model%T
%J International Journal of Computer Applications
%V 178
%N 31
%P 1-6
%R 10.5120/ijca2019918401
%I Foundation of Computer Science (FCS), NY, USA
Abstract
This paper presents a Neural Network based Nepali Speech Recognition model. RNN (Recurrent Neural Networks) is used for processing sequential audio data. CTC (Connectionist Temporal Classification) [1] technique is applied allowing RNN to train over audio data. CTC is a probabilistic approach of maximizing the occurrence probability of the desired labels from RNN output. After processing through RNN and CTC layers, Nepali text is obtained as output. This paper also defines a character set of 67 Nepali characters required for transcription of Nepali speech to text.
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