A novel voice recognition model based on HMM and fuzzy PPM

Hidden Markov Model (HMM) is a robust statistical methodology for automatic speech recognition. It has being tested in a wide range of applications. A prediction approach traditionally applied for the text compression and coding, Prediction by Partial Matching (PPM) which is a finite-context statistical modeling technique and can predict the next characters based on the context, has shown a great potential in developing novel solutions to several language modeling problems in speech recognition. These two different approaches have their own special features respectively contributing to voice recognition. However, no work has been reported in integrating them at attempt to forming a hybrid voice recognition scheme. To take the advantages of strengths of these two approaches, we propose a hybrid speech recognition model based on HMM and fuzzy PPM, which has demonstrated by the experiment competitive and promising performance in speech recognition.