ISOLATED SPEECH RECOGNITION SYSTEM FOR TAMIL LANGUAGE USING STATISTICAL PATTERN MATCHING AND MACHINE LEARNING TECHNIQUES

Abstract

In recent years, speech technology has become a vital part of our daily lives. Various techniques have been proposed for developing Automatic Speech Recognition (ASR) system and have achieved great success in many applications. Among them, Template Matching techniques like Dynamic Time Warping (DTW), Statistical Pattern Matching techniques such as Hidden Markov Model (HMM) and Gaussian Mixture Models (GMM), Machine Learning techniques such as Neural Networks (NN), Support Vector Machine (SVM), and Decision Trees (DT) are most popular. The main objective of this paper is to design and develop a speaker-independent isolated speech recognition system for Tamil language using the above speech recognition techniques. The background of ASR system, the steps involved in ASR, merits and demerits of the conventional and machine learning algorithms and the observations made based on the experiments are presented in this paper. For the above developed system, highest word recognition accuracy is achieved with HMM technique. It offered 100% accuracy during training process and 97.92% for testing process.

Keywords

• Tamil speech recognition
• Dynamic time warping
• Hidden Markov model
• Gaussian mixture models
• Support vector machine (SVM)
• Neural networks
• Decision trees