The research of the existing speech recognition is based on speech feature parameter, acco-rding to the shortage of poor anti noise and larger storage capacity, etc. So, curve interpolation has been introduced into speech feature parameter extraction to enhance that. Refer to the speech spectrum dynamic changes and the short-time energy smooth stationary characteristics of speech signal, this paper puts forward and designs an arithmetic of speech feature parameter extraction based on interpolation, constructs the feature parameter extraction and personal identification scheme based on speech, and also designs critical modules algorithm. The detail process of feature parameter extraction: firstly, it creates two-dimensional coordinate for each frame data. Then, according to two-dimensional coordinate, it performs Lagrange cubic interpolation for segmentation the data in a signal frame. Get the interpolation coefficient, average the interpolation coefficient for a signal frame, here the average value is seen as the feature parameter for each frame. Lastly, the each frame’s feature parameter is connected in series to form feature parameter of the speech segment. The arithmetic has been simulated an experiment, in order to confirm the applicability and feasibility. The results illustrates the method has preferable anti noise performance, especially expression and storage for overall speech segment feature parameter show more obvious advantages.
Dynamic Speech Feature Parameter Extraction Based on Fitting
