01696nas a2200241   4500000000100000000000100001008004100002260001200043653001700055653002500072653002300097653002700120653004000147653002400187100001700211700002300228245008400251856009500335300001000430490000600440520099400446022001401440       2018                            d  c06/201810aA Priori SNR10aSpectral Restoration10aSpeech Enhancement10aSpeaker Identification10aMel Frequency Cepstral Coefficients10aVector Quantization1 aNasir Saleem1 aTayyaba Gul Tareen00aSpectral Restoration Based Speech Enhancement for Robust Speaker Identification  uhttp://www.ijimai.org/journal/sites/default/files/files/2018/01/ijimai_5_1_4_pdf_16417.pdf  a34-390 v53 aSpectral restoration based speech enhancement algorithms are used to enhance quality of noise masked speech for robust speaker identification. In presence of background noise, the performance of speaker identification systems can be severely deteriorated. The present study employed and evaluated the Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimators with modified a priori SNR estimate prior to speaker identification to improve performance of the speaker identification systems in presence of background noise. For speaker identification, Mel Frequency Cepstral coefficient and Vector Quantization is used to extract the speech features and to model the extracted features respectively. The experimental results showed significant improvement in speaker identification rates when spectral restoration based speech enhancement algorithms are used as a pre-processing step. The identification rates are found to be higher after employing the speech enhancement algorithms.  a1989-1660