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In this paper, an improved strategy for automated text dependent speakeridentification system has been proposed in noisy environment. Theidentification process incorporates the Neuro- Genetic hybrid algorithm withcepstral based features. To remove the background noise from the sourceutterance, wiener filter has been used. Different speech pre-processingtechniques 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 beenused in the learning and identification purposes. Features are extracted byusing different techniques to optimize the performance of the identification.According to the VALID speech database, the highest speaker identification rateof 100.000 percent for studio environment and 82.33 percent for officeenvironmental conditions have been achieved in the close set text dependentspeaker identification system.

Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions