Open Access

ARTICLE

Enhanced Marathi Speech Recognition Facilitated by Grasshopper Optimisation-Based Recurrent Neural Network

by Ravindra Parshuram Bachate¹, Ashok Sharma², Amar Singh³, Ayman A. Aly⁴, Abdulaziz H. Alghtani⁴, Dac-Nhuong Le^5,6,*

1 School of Computer Science and Engineering, Lovely Professional University, Punjab, 144001, India
2 Department of Computer Science and IT, University of Jammu, Jammu and Kashmir, 180006, India
3 School of Computer Applications, Lovely Professional University, Punjab, 144001, India
4 Department of Mechanical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia
5 School of Computer Science, Duy Tan University, Danang, 550000, Vietnam
6 Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam

* Corresponding Author: Dac-Nhuong Le. Email:

Computer Systems Science and Engineering 2022, 43(2), 439-454. https://doi.org/10.32604/csse.2022.024214

Received 09 October 2021; Accepted 10 November 2021; Issue published 20 April 2022

Abstract

Communication is a significant part of being human and living in the world. Diverse kinds of languages and their variations are there; thus, one person can speak any language and cannot effectively communicate with one who speaks that language in a different accent. Numerous application fields such as education, mobility, smart systems, security, and health care systems utilize the speech or voice recognition models abundantly. Though, various studies are focused on the Arabic or Asian and English languages by ignoring other significant languages like Marathi that leads to the broader research motivations in regional languages. It is necessary to understand the speech recognition field, in which the major concentrated stages are feature extraction and classification. This paper emphasis developing a Speech Recognition model for the Marathi language by optimizing Recurrent Neural Network (RNN). Here, the preprocessing of the input signal is performed by smoothing and median filtering. After preprocessing the feature extraction is carried out using MFCC and Spectral features to get precise features from the input Marathi Speech corpus. The optimized RNN classifier is used for speech recognition after completing the feature extraction task, where the optimization of hidden neurons in RNN is performed by the Grasshopper Optimization Algorithm (GOA). Finally, the comparison with the conventional techniques has shown that the proposed model outperforms most competing models on a benchmark dataset.

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