HMM/ANN hybrid model for continuous Malayalam speech recognition

Abstract

Abstract This paper describes the development of a context independent, small vocabulary, connectionist-statistical continuous Malayalam speech recognition system which combines the time normalization property of Hidden Markov Models (HMMs) with the superior discriminative ability of Artificial Neural Networks (ANNs). In this work, the HMM based phoneme models use the emission probabilities estimated from the posterior probabilities obtained through Multi Layer Perceptrons. We evaluated the performance of our proposed system on a small vocabulary, speaker independent continuous Malayalam speech corpus and our system has produced a promising result of 86.67% word and 66.67% sentence recognition rates. This is the first reported result for a Malayalam speaker independent continuous speech recognizer based on an HMM/ANN hybrid framework.