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Nepali Speech Recognition using RNN-CTC Model

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 31
Year of Publication: 2019
Authors:
Paribesh Regmi, Arjun Dahal, Basanta Joshi
10.5120/ijca2019918401

Paribesh Regmi, Arjun Dahal and Basanta Joshi. Nepali Speech Recognition using RNN-CTC Model. International Journal of Computer Applications 178(31):1-6, July 2019. BibTeX

@article{10.5120/ijca2019918401,
	author = {Paribesh Regmi and Arjun Dahal and Basanta Joshi},
	title = {Nepali Speech Recognition using RNN-CTC Model},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2019},
	volume = {178},
	number = {31},
	month = {Jul},
	year = {2019},
	issn = {0975-8887},
	pages = {1-6},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume178/number31/30732-2019918401},
	doi = {10.5120/ijca2019918401},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, 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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Keywords

Artificial Intelligence, Machine Learning, Automatic Speech Recognition, Recurrent Neural Network, Connectionist Temporal Classification, Softmax, Hidden Markov Model, Nepali Speech Recognition, Long-Short Term Memory (LSTM), Backpropagation, Character Error Rate