چکیده (انگلیسی):

This study presents an experimental evaluation of Discrete
Wavelet Transforms for use in speaker identification. The features are
tested using speech data provided by the CHAINS corpus. This system
consists of two stages: Feature extraction stage and the identification
stage. Parameters are extracted and used in a closed-set text-independent
speaker identification task. In this study the signals are pre-processed
and features are extracted using discrete wavelet transforms. The energy
of the wavelet coefficients are used for training the Gaussian Mixture
Model. Daubechies wavelets are used and the speech samples are analyzed
using 8 levels of decomposition.