The Application of Hidden Markov Models in Speech Recognition

Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabulary continuous speech recognition (LVCSR) systems are based on HMMs. Whereas the basic principles underlying HMM-based LVCSR are rather straightforward, the approximations and simplifying assumptions involved in a direct implementation of these principles would result in a system which has poor accuracy and unacceptable sensitivity to changes in operating environment. Thus, the practical application of HMMs in modern systems involves considerable sophistication. The aim of this review is first to present the core architecture of a HMM-based LVCSR system and then describe the various refinements which are needed to achieve state-of-the-art performance. These 

• Type: Journal
• Year: 2007
• Where: FTSIG
• Authors: Mark J. F. Gales, Steve Young
• Keywords: FTSIG 2007, HMM-based LVCSR, Markov Models, Time-varying Spectral Vector