Title: Automatic speech recognition of Gujarati digits using wavelet coefficients in machine learning algorithms

Authors: Purnima Pandit; Shardav Bhatt

Addresses: Department of Applied Mathematics, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Vadodara, Gujarat, India ' School of Engineering and Technology, Navrachana University, Vadodara, Gujarat, India

Abstract: In today's world, automatic speech recognition (ASR) is an important task implemented via machine learning (ML) to assist artificial intelligence (AI). It has diverse applications such as human-machine interactions, hands-free computing, voice search, domestic appliance control and many more. Speech recognition in an Indian regional language becomes a very necessary task in order to facilitate people, who can communicate only using their mother tongue and the disabled ones. In this article, we have proposed and performed experiments of speech recognition for Gujarati language, particularly for Gujarati digits. The recorded speech is pre-processed and then speech features are extracted from it using Mel-frequency discrete wavelet coefficient (MFDWC). These features are trained using artificial neural networks (ANN) for classification. Two ANN architectures namely, multi-layer perceptrons (MLP) and radial basis function networks (RBFN) are used for training and recognition. The experimental results obtained in this work are compared with our previous experimental results.

Keywords: automatic speech recognition; ASR; machine learning; ML; artificial neural networks; ANN; radial basis function networks; RBFN.

DOI: 10.1504/IJICA.2023.134184

International Journal of Innovative Computing and Applications, 2023 Vol.14 No.4, pp.191 - 200

Received: 09 Jul 2021
Received in revised form: 14 Feb 2022
Accepted: 30 May 2022
Published online: 13 Oct 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article