An Ensemble Learning Based Bangla Phoneme Identification System Using LSF-G Features

Technology has evolved a lot in the last decade, and various devices have come up for assisting us in our day-to-day life. There has always been a need for simplifying the User Interfaces (UI) of such devices so that they can be easily interacted with, and a speech based UI can be a potential solution. Speech recognition is the task of identification of words from voice signals. Every language consists of a set of atomic sounds called Phonemes which builds up the entire vocabulary of that language. Speech recognition in Bangla is rather a complicated task due to the complex nature of the language like the presence of compound characters. In this paper, a Bangla Phoneme recognition system is proposed towards the development of a Bangla Speech recognition system based on Line Spectral Frequency-Grade (LSF-G) features derived from standard line spectral frequency values. The system has been tested on a Bangla Swarabarna Phoneme dataset of 3290 clips and an accuracy of 94.01% has been obtained with an Ensemble learning based approach.