The Research of Feature Extraction Algorithm for Speech Recognition and the Realization
|Course||Computer Software and Theory|
|Keywords||Speech recognition Endpoint detection Mel Frequency Cepstral Coefficients Dynamic Time Warping|
The voice signal having a strong time-varying characteristics , and the characteristics of the speech signal in a shorter time interval can be regarded as essentially unchanged , which is an important starting point of the speech signal processing . The level of the speech recognition rate , and also depends on the accuracy and robustness of the voice signal feature extraction . Therefore , the voice signal feature extraction plays a decisive role in the speech signal processing applications . The thesis studied the basics of speech recognition , including speech recognition principle ; basic knowledge of the speech signal processing ; speech recognition and training methods . On this basis, this paper completed work : 1 , focuses on the widely used Mel frequency cepstral coefficients (MFCC) parameters , the 24 -dimensional MFCC parameters , for example , changes in component analysis of the higher order parameter is missing the recognition rate identify a higher order MFCC parameters are sensitive to noise , and in the recognition rate changed little in the case of 24 -dimensional MFCC parameters optimized combination . 2, using the VC model based on dynamic time warping (DTW) to achieve a connection string of digits speech recognition system and experimental analysis . The basic configuration of the composition of the system module and the voice recognition system model . In the realization of selected Mel frequency coefficients (MFCC) . 3 , the experiment found that the Chinese digital easy to confuse the issues , and made ??improvements in the template training methods and reference template is proposed to use multiple pairs of feature vector sequences robust training and acoustic vowel segmentation to construct a reference template method. Finally, this paper studies the Chinese continuous speech recognition acoustic modeling methods identify Chinese easily confused words . This article through experiments and research on the part of the actual speech recognition system for further work to develop practical speech recognition system to do the basic work .