Research on Intelligent Robot Command-Word Recognition System Based on Feature Extraction

According to the problem of the low recognition rate of speaker-independent recognition in intelligent robot, a kind of endpoint detection algorithm with double threshold is adopted and the speech endpoint can be detected accurately. The mixed parameter of Mel Frequency Cepstral Coefficients (MFCC) and fractal dimension is used as the feature parameter, and the intelligent robot command-word recognition system based on Hidden Markov Models (HMM) is realized. The recognition effect achieves above 85%. Then the performance of MFCC and the mixed parameter of MFCC and fractal dimension is contrasted and analyzed. The experiment result shows that the system recognition rate is improved by the algorithm of mixed parameter, and the system recognition performance is optimized.