Spectral Restoration Based Speech Enhancement for Robust Speaker Identification

TitleSpectral Restoration Based Speech Enhancement for Robust Speaker Identification
Publication TypeJournal Article
Year of Publication2018
AuthorsSaleem, N., and T. G. Tareen
JournalInternational Journal of Interactive Multimedia and Artificial Intelligence
ISSN1989-1660
IssueRegular Issue
Volume5
Number1
Date Published06/2018
Pagination34-39
Abstract

Spectral 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.

KeywordsA Priori SNR, Mel Frequency Cepstral Coefficients, Speaker Identification, Spectral Restoration, Speech Enhancement, Vector Quantization
DOI10.9781/ijimai.2018.01.002
URLhttp://www.ijimai.org/journal/sites/default/files/files/2018/01/ijimai_5_1_4_pdf_16417.pdf
AttachmentSize
ijimai_5_1_4.pdf1.86 MB