P S Subhashini Pedalanka, Dr M. SatyaSai Ram, Dr Duggirala Sreenivasa Rao 03 2 Speaker Recognition is essential in the field of authentication and surveillance to validate the users identity using extracted feature characteristics of audio speech signal In this work the speaker recognitions performed by deep neural networkRadial Basis Function DNNRBF Initially the available speech signals are preprocessed to remove the noise from the input signal The noise removal in the input signal is performed by wiener filter From this preprocessed signal Mel frequency cepstral coefficients MFCC features are extracted The ivector is estimated from the Gaussian Mixture Model GMM super vector in which the dimensionality of extracted features is reducedExtracted ivector features are then injected within classifier for recognizing the specific speaker Based on these extracted features the speakers are recognized by Adaptive Harris Hawk Optimization AHHO based DNNRBFin an appropriate manner The performance of this speaker recognition process is evaluated with TIMIT Texas InstrumentsMassachusetts Institute of Technologydataset Some of the performance metrics like precision accuracy and recall are evaluated to evaluate the effectiveness of this proposed techniqueThe proposed speaker recognition technique is evaluated with various performance measures such as EER precision recall and accuracy The accuracy precision and recall values attained by proposed AHHO based DNNRBF is 9492 8987 and 9467 respectively The presence of adaptive optimization approach improves the performance of DNNRBF in speaker recognition The implementation process is performed in Mat lab platform Copyright (c) World Journal of Pharmaceutical Seiences. All rights reserved 2026