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

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.