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

Islam, Md. Rabiul and Rahman, Md. Fayzur (2009) Improvement of Text Dependent Speaker Identification System Using Neuro-Genetic Hybrid Algorithm in Office Environmental Conditions. [Journal (Paginated)]

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Abstract

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% for studio environment and 82.33% for office environmental conditions have been achieved in the close set text dependent speaker identification system.

Item Type:Journal (Paginated)
Keywords:Bio-informatics, Robust Speaker Identification, Speech Signal Pre-processing, Neuro-Genetic Hybrid Algorithm
Subjects:Computer Science > Artificial Intelligence
ID Code:6688
Deposited By: International Journal of Computer Science Issues, IJCSI
Deposited On:14 Nov 2009 11:35
Last Modified:11 Mar 2011 08:57

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