Voice recognition systems have found widespread use in applications such as tele-shopping, tele-banking, information services, home automation, voice message security, and voice call dialing, which allows a driver to make calls safely while driving.
This project presents the development of a high performance speech recognition system using human voice models. Recognizing the behavior of the human ear, the Mel Frequency Cepstral Coefficient (MFCC) method is used to develop the system capability for feature extraction. Vector quantization optimized by the Linde Buzo Gray (LGB) algorithm is used for feature matching. Experimental results show that the system has over 90% success rate in the noise-free case, but the system performance deteriorates in the presence of noise. The system, however, has better recognition ability when the noise signal consists of harmonic components, as compared to a non-stationary, non-harmonic signal.
|Commitee:||Ary, James, Tran, Boi|
|School:||California State University, Long Beach|
|School Location:||United States -- California|
|Source:||MAI 55/06M(E), Masters Abstracts International|
|Subjects:||Engineering, Electrical engineering|
|Keywords:||Mel Frequency Cepstral Coefficient|
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