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International Journal of Innovative Research and Development, Vol 2, No 1 (2013), Pagination: 48-55
Abstract
Human speech is our most natural form of communication and conveys both meaning and identity. The identity of a speaker can be determined from the information contained in the speech signal through speaker identification. Speaker identification is concerned with identifying unknown speakers from a database of speaker models previously enrolled in the system. We are using text dependent speaker identification i.e. speaker will have to speak predefined password. The general process of speaker identification involves two main stages. The first stage extracts features from speakers. And second stage involves processing the identity of a speaker using features extracted from the speech. Several techniques available for feature extraction including Linear Predictive Coding (LPC), Mel-Frequency Cepstral Coefficients and LPC Cepstral coefficients. These features are used with a classification technique to create a speaker model. In this project we are using the Mel Frequency Cepstral Coefficients (MFCC) technique to extract features from the speech signal and compare the unknown speaker with the exits speaker in the database. For matching we are using Vector Quantization which is commonly used in speaker identification producing reliable results.
Keywords
Human Speech, Mel Frequency Cepstral Coefficients, Linear Predictive Coding (LPC), Database, Vector Quantization
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