  In this paper, an improved strategy for automated text dependent speaker
identification system has been proposed in noisy environment. The
identification process incorporates the Neuro- Genetic hybrid algorithm with
cepstral based features. To remove the background noise from the source
utterance, wiener filter has been used. Different speech pre-processing
techniques such as start-end point detection algorithm, pre-emphasis filtering,
frame blocking and windowing have been used to process the speech utterances.
RCC, MFCC, MFCC, MFCC, LPC and LPCC have been used to extract the features.
After feature extraction of the speech, Neuro-Genetic hybrid algorithm has been
used in the learning and identification purposes. Features are extracted by
using different techniques to optimize the performance of the identification.
According to the VALID speech database, the highest speaker identification rate
of 100.000 percent for studio environment and 82.33 percent for office
environmental conditions have been achieved in the close set text dependent
speaker identification system.
