Research Of Speech Recognition Technology Based On Hidden Markov Model

Speech recognition is the technique that the machine changes the speech signal of human to me corresponding text or command by recognition and understanding process. The fundamentality purpose is to design the machine with hearing ability, it can directly accept and understand human's intention, and make out the relevant reaction.Using speech signal as research object, speech recognition is an important research direction of the speech signal processing and it is an embranchment of pattern recognition, too. It involves linguistics, computer science, signals processing, physiology and psychology etc, and even relates to body language. The final goal is to realize the natural communication between human and machine. Speech recognition has a wide application future. It has made a full application in dictation machine, telephone inquiry system and home application control etc.Hidden Markov Model is the mainstream algorithm in the filed of speaker recognition. Hidden Markov Model use hidden state to associate the relatively steady pronouncing unit, and describe the change of pronouncing by state staying or transfer. For simply research, HMM assume the time of continuous states staying obey geometry distributing. However, this is not always the true. This paper introduce the Duration Distribution Based HMM, it can describe timing correlation of speech signal.In this thesis, we study to build a simple speaker-depended large-vocabulary Chinese spoken word recognition system based on HTK as speech process platform. Then the system is utilized to compare recognized result by adopting different types of feature parameters, and try to find the best suit one. The accuracy rating arrive at preferable level when use initial and final model as the basic speech unit to built HMM model, and set the state number 3 and 5 respectively, and output observation mixture Gauss dimension set to 7. The correct rate doesn't rise obviously even if we continue add state number and dimension, but only slow down the recognition speed. At last, the experiments implement the Duration Distribution Based HMM by modifying HTK source code and result shows it have a significant improvement of accuracy rating.