| Summary: | 碩士 === 國立臺北科技大學 === 自動化科技研究所 === 93 === The main contribution of this thesis is to develop a real-time speaker recognition system with Speaker Model Mel-Frequency Cepstral Coefficients (SMMFCC) derived from Fast Fourier Transform (FFT). Back-Propagation Neural Network is used on ARM-based embedded system platform to perform the speaker recognition function. Due to the limitations of computing capability and memory of embedded systems, the features extracted from speaker model are reduced.
In order to overcome the computation limitation, a client - server architecture is proposed in this thesis. In this architecture, the server deals with the Neural Network training process that requires a great deal of computation, while the client performs the real-time speaker recognition based on the updated weights of neural network which is retrieved from the server.
The experimental results show that the average recognition rate of this system is more than 90% and the recognition time is less than 3 seconds. The proposed speaker recognition system can be generally applied to home security, office security, factory security systems, etc.
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