Automatic Speaker Recognition Using Fuzzy Vector Quantization
نویسندگان
چکیده
Speaker recognition (SR) is a dynamic biometric task. SR is a multidisplinary problem that encompasses many aspects of human speech, including speech recognition, language recognition, and speech accents. This technique makes it possible to use the speaker’s voice to verify his/her identity and provide controlled access to services. The Mel-frequency extraction method is leading approach for speech feature extraction. In this thesis, a new algorithm has been proposed which incorporates FVQ and DCT based MFCC feature extraction method. The proposed system will be improved the performance of SR through MFCC and FVQ methods. The FVQ performance result will be compared with K means quantization in terms of
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