Recent Trends in Speech Recognition Systems

Abstract

Ways of improving the accuracy and efficiency of automatic speech recognition (ASR) systems have been a long term goal of researchers to develop the natural language man machine communication interface. In widely used statistical framework of ASR, feature extraction technique is used at the front-end for speech signal parameterization, and hidden Markov model (HMM) is used at the back-end for pattern classification. This chapter reviews classical and recent approaches of Markov modeling, and also presents an empirical study of few well known methods in the context of Hindi speech recognition system. Various performance issues such as number of Gaussian mixtures, tied states, and feature reduction procedures are also analyzed for medium size vocabulary. The experimental results show that using advanced techniques of acoustic models, more than 90% accuracy can be achieved. The recent advanced models outperform the conventional methods and fit for HCI applications.