Linguistic effects on speaker discrimination.

2010 ◽  
Vol 128 (4) ◽  
pp. 2349-2349
Author(s):  
Chandan R. Narayan ◽  
Molly Babel
Keyword(s):  
Speaker Discrimination

scholarly journals Auditory speaker discrimination using modal and falsetto speech

2021 ◽  
Vol 42 (1) ◽  
pp. 62-64
Author(s):  
Kanae Amino ◽  
Maki Shono ◽  
Takayuki Arai
Keyword(s):  
Speaker Discrimination

scholarly journals A language-familiarity effect for speaker discrimination without comprehension

2014 ◽  
Vol 111 (38) ◽  
pp. 13795-13798 ◽  
Author(s):  
D. Fleming ◽  
B. L. Giordano ◽  
R. Caldara ◽  
P. Belin
Keyword(s):  
Familiarity Effect ◽  
Language Familiarity ◽  
Speaker Discrimination

scholarly journals Towards understanding speaker discrimination abilities in humans and machines for text-independent short utterances of different speech styles

2018 ◽  
Vol 144 (1) ◽  
pp. 375-386 ◽  
Author(s):  
Soo Jin Park ◽  
Gary Yeung ◽  
Neda Vesselinova ◽  
Jody Kreiman ◽  
Patricia A. Keating ◽  
...  
Keyword(s):  
Speech Styles ◽  
Speaker Discrimination ◽  
Short Utterances

scholarly journals Signal processing for ubiquitous biometric systems

2021 ◽  
Author(s):  
Danoush Hosseinzadeh
Keyword(s):  
Computer Security ◽  
Speaker Identification ◽  
Recognition System ◽  
Improve Performance ◽  
Identification Scheme ◽  
Verification Method ◽  
Biometric Systems ◽  
Speaker Discrimination ◽  
First Time ◽  
Timit Database

This work presents two hardware independent and ubiquitous biometric solutions that can significantly improve security for computer and telephone related applications. Firstly, for computer security, a GMM based keystroke verification method is proposed along with the up-up keystroke latency (UUKL) feature which is being used for the first time. This method can verify the identity of users based on their typing pattern and achieved a FAR of 5.1%, a FRR of 6.5%, and a EER of 5.8% for a database of 41 users. Due to many inconsistencies in previous works, a new keystroke protocol has also been proposed. This protocol makes a number of recommendations concerning how to improve performance, reliability, and accuracy of any keystroke recognition system. Secondly, a GMM based text-independent speaker identification scheme is also proposed that utilizes novel spectral features for better speaker discrimination. Based on 100 users from the TIMIT database, these features achieved an identification error of 1.22% by incorporating information about the source of the speech signal. This represents a 6% improvement over the MFCC based features.

Acoustic correlates of speaker discrimination in English.

2008 ◽  
Vol 124 (4) ◽  
pp. 2457-2457
Author(s):  
Chandan Narayan ◽  
Jiahong Yuan
Keyword(s):  
Acoustic Correlates ◽  
Speaker Discrimination
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