Speech Recognition Based On Wavelet Analysis

The application of speech recognition technology allows the input speech signal to be changed into speech code. With the technology, not only the data of the speech, which is transferred and storied in code mode, is less than that in original way, but the speech code is easier processed by computer or other information process unit. Therefore, the speech recognition technology can be applied in many fields, for example, a machine can understand out language. Efficient speeches de-noise which is a research focus in IT is meaningful for real world and has high theoretical value.The theme is about de-noising of speech with noise and speech recognition. Firstly, the feature of speech signal and noise is introduced, and then the components of the speech recognition system, such as preprocessing, means of speech signal analysis, feature extraction, the training and the matching of speech template, are discussed. To increase the rate of speech recognition, the parameter of speech feature should be extracted accurately, signal de-noise is the best way to achieve the goal.The 'sym8' wavelet and 'Heusure' threshold rule are chosen. Under the 'sln' readjustment method, hard, soft and double threshold are separately adopted in the experiments of different layer wavelet. The results of the experiment support 5 layers criterion decomposition with the double threshold, with which we can get good de-noise effect and reduce the lost of signal. And the study provide a effective method of wavelet and threshold selection...