Speaker identification using vector quantization and dynamic time warping
Speaker Recognition is a system in which an individual is recognized based on voice signals. In this article, we provide a brief overview of the history of the methodology used in speech recognition for pattern identification. A method for modelling a speaker recognition system was also addressed and suggested, which involves the pre-processing stage, the extraction phase of features and the classification phase of patterns. LPCC and MFCC are used in this method as text-related speech recognition and the experiment uses vector quantization and dynamic time warping (DTW) to compare a speaking identity recognition rate for LPCC, MFCC or a combination of LPCC and MFCC. This reflects the higher acceptance levels for LPCC and MFCC.