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The impact of compression of speech signal, background noise and acoustic disturbances on the effectiveness of speaker identification
Author(s): K. Kamiński; A. P. Dobrowolski
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Paper Abstract

The paper presents the architecture and the results of optimization of selected elements of the Automatic Speaker Recognition (ASR) system that uses Gaussian Mixture Models (GMM) in the classification process. Optimization was performed on the process of selection of individual characteristics using the genetic algorithm and the parameters of Gaussian distributions used to describe individual voices. The system that was developed was tested in order to evaluate the impact of different compression methods used, among others, in landline, mobile, and VoIP telephony systems, on effectiveness of the speaker identification. Also, the results were presented of effectiveness of speaker identification at specific levels of noise with the speech signal and occurrence of other disturbances that could appear during phone calls, which made it possible to specify the spectrum of applications of the presented ASR system.

Paper Details

Date Published: 20 April 2017
PDF: 12 pages
Proc. SPIE 10418, XI Conference on Reconnaissance and Electronic Warfare Systems, 104180L (20 April 2017); doi: 10.1117/12.2269338
Show Author Affiliations
K. Kamiński, Military Univ. of Technology (Poland)
A. P. Dobrowolski, Military Univ. of Technology (Poland)


Published in SPIE Proceedings Vol. 10418:
XI Conference on Reconnaissance and Electronic Warfare Systems
Jerzy Lopatka, Editor(s)

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