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.
HMM PPM voice recognition fuzzy logic statistical model
Jackson Zhang Bruce Wang
Software Engineer, G&PS (R&D) Chengdu Site, Motorola China
国际会议
2010 IEEE 10th International Conference on Signal Processing(第十届信号处理国际会议 ICSP 2010)
北京
英文
637-640
2010-08-24（万方平台首次上网日期，不代表论文的发表时间）