| Since the speech signals have strong time variance, it is an important springboard of speech signal processing that the voicing features can only be considered invariable in little time interval. The rate of speech recognition depends on the accuracy and robustness of voice feature extraction. So, extract the voicing features of speech signal play an important role in speech signal processing.First, the paper focus on fundamentals of speech recognition, including: principle of speech recognition, basic knowledge of speech signal processing, and all kinds of methods of speech training and recognition. Based on the basic theories, the paper has most works as follow:1,The paper focus on MFCC which widely used, as 24-dimensional MFCC terms example, analysis the impact of lacking of high MFCC terms on speech-recognition rates by changing the number of the terms, find out the high terms which not sensitive to noises are given, and optimize the 24-dimensional MFCC terms under recognition rates change is not big situation.2,Use Visual C++ 6.0 to implement a figure string speech-recognition system which based on DTW model, and makes an experiment on this system. The system is consistent with the model of the speech-recognition system. The paper select Mel Frequency Cepstrum Coefficient (MFCC) as feature terms.3,In experiment, it finds that the Chinese digital easy to confuse, in two aspects, training and reference template, we have made improved, and present a way of use more vector sequences to robust train, and a method by dividing the initial and final into two segments, and construct a reference template.4,Finally, the paper researches acoustics modeling method of Chinese continuous speech-recognition, and indicates the method to recognize the word which easily confused in Chinese words.Through the experiment and research of the actual speech-recognition system, it carries out the fundamental and exploring research for the further application of speech-recognition system. |
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