Native and Non-Native Marathi Numerals Recognition using LPC and ANN

Abstract

Speech recognition is gaining an increasing research interest in the last five decades. Speech processing is considered as an interdisciplinary branch of electronics and computer science domain. It considers speech as an input and converts it into the corresponding text. This paper describes the design and development of Marathi Numeric Speech Dataset. Marathi Numbers (Ank) ranging from Shunya(0) to Nau(9) and are taken into consideration for recording. Speech samples are collected from 50 native and 50 non-native speakers of Marathi language. The dataset remains as a gender balanced since it is recorded from 50 females and 50 male speakers. The age of speakers will affect the speech. Therefore, 5 different age groups such as 11-20, 21-30, 31-40, 41-50 and 51-60 are considered. Native and non-native speakers are selected to obtain ample amount of variations in the pronunciation of Marathi numerals. Feature extraction and feature matching technique plays a vital role for speech recognition and here LPC (Linear Predictive Coding) is used for extracting features from samples, whereas ANN (Artificial neural network) is used to classify them. Experimental specifications and results are also discussed. This research work has attempted to design and develop a speech recognition system, which can understand Marathi Ank (Numbers) and identify them accurately.