scholarly journals Correction To: Hollien, H. (2013). Barriers to Progress in Speaker Identification with Comments on the Trayvon Martin Case. Linguistic Evidence In Security, Law And Intelligence, 1(1), 76-98. doi:10.5195/lesli.2013.3

2014 ◽  
Vol 2 (1) ◽  
Author(s):  
Carole Chaski
Keyword(s):  
Speaker Identification ◽  
Linguistic Evidence ◽  
Trayvon Martin

Seeing Is Believing: Visual Media Bias in the Trayvon Martin Case

2012 ◽  
Author(s):  
Ashley E. Warchol ◽  
Sarah Bergquist ◽  
J. Taylor Thatcher ◽  
Peter E. Jaberg
Keyword(s):  
Media Bias ◽  
Visual Media ◽  
Trayvon Martin

Method for measuring distortions of a speech signal during its transmission over a communication channel to a biometric identification system

2020 ◽  
pp. 65-72
Author(s):  
V. V. Savchenko ◽  
A. V. Savchenko
Keyword(s):  
Speech Signal ◽  
Communication Channel ◽  
Speaker Identification ◽  
A Priori ◽  
Small Samples ◽  
Practical Implementation ◽  
Identification System ◽  
New Information ◽  
Discrimination Criterion ◽  
A Priori Uncertainty

This paper is devoted to the presence of distortions in a speech signal transmitted over a communication channel to a biometric system during voice-based remote identification. We propose to preliminary correct the frequency spectrum of the received signal based on the pre-distortion principle. Taking into account a priori uncertainty, a new information indicator of speech signal distortions and a method for measuring it in conditions of small samples of observations are proposed. An example of fast practical implementation of the method based on a parametric spectral analysis algorithm is considered. Experimental results of our approach are provided for three different versions of communication channel. It is shown that the usage of the proposed method makes it possible to transform the initially distorted speech signal into compliance on the registered voice template by using acceptable information discrimination criterion. It is demonstrated that our approach may be used in existing biometric systems and technologies of speaker identification.

Bilingual speaker identification: Chinese and English

2015 ◽  
Vol 22 (1) ◽  
pp. 57-78 ◽  
Author(s):  
Peggy P.K. Mok ◽  
Robert Bo Xu ◽  
Donghui Zuo
Keyword(s):  
Speaker Identification ◽  
Bilingual Speaker ◽  
Chinese And English

New Feature Vectors using GFCC for Speaker Identification

2018 ◽  
Vol 6 (8) ◽  
pp. 243
Author(s):  
A. Nagesh
Keyword(s):  
Speaker Recognition ◽  
Speaker Identification ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
Extraction Methods ◽  
Identification System ◽  
Identification Performance ◽  
Feature Vectors ◽  
Overall Performance ◽  
New Feature

The feature vectors of speaker identification system plays a crucial role in the overall performance of the system. There are many new feature vectors extraction methods based on MFCC, but ultimately we want to maximize the performance of SID system.  The objective of this paper to derive Gammatone Frequency Cepstral Coefficients (GFCC) based a new set of feature vectors using Gaussian Mixer model (GMM) for speaker identification. The MFCC are the default feature vectors for speaker recognition, but they are not very robust at the presence of additive noise. The GFCC features in recent studies have shown very good robustness against noise and acoustic change. The main idea is  GFCC features based on GMM feature extraction is to improve the overall speaker identification performance in low signal to noise ratio (SNR) conditions.

A Robust Speaker Identification Method Based on the Wavelet Filter Banks

2002 ◽  
Vol 9C (4) ◽  
pp. 459-466
Author(s):  
Dae-Jong Lee ◽  
Geun-Chang Gwak ◽  
Jeong-Ung Yu ◽  
Myeong-Geun Jeon
Keyword(s):  
Filter Banks ◽  
Speaker Identification ◽  
Wavelet Filter ◽  
Identification Method ◽  
Robust Speaker Identification
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