101 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 6 DOI: 10.4018/978-1-4666-0954-9.ch006 1. INTRODUCTION Human computer interaction through natural lan- guage conversational interface plays an important role in improving the usage of computers for the common man. The success of such speech enabled man machine communication interface depends mainly upon the performance of automatic speech recognition system. State-of-the-art ASR systems use statistical pattern classification approach, hav- ing the two well known phases: feature extraction and pattern classification. In the architecture of ASR, feature extraction phase comes under front-end, that converts the recorded waveform to some form of acoustic representation known as feature vectors. Back- end covers the different statistical models such as acoustic models and language models, along with searching methods and adaptation techniques for classification. The features are based on time- frequency representation of acoustic signals, which are computed at regular intervals (e.g., every 10ms). The feature vectors are decoded into linguistic units like word, syllable, and phones R. K. Aggarwal National Institute of Technology Kurukshetra, India M. Dave National Institute of Technology Kurukshetra, India 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 re- duction 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.