10.1371/journal.pone.0158520.g008
Md. Atiqul Islam
Md.
Atiqul Islam
Wissam A. Jassim
Wissam A.
Jassim
Ng Siew Cheok
Ng Siew
Cheok
Muhammad Shamsul Arefeen Zilany
Muhammad Shamsul Arefeen
Zilany
Text-dependent speaker identification performance of the proposed and existing methods using the UM database.
Public Library of Science
2016
2- D neurograms
Gamma-tone frequency cepstral coefficients
Mel-frequency cepstral coefficients
Auditory Periphery Speaker identification
speaker identification system
speech processing applications
Robust Speaker Identification System
speaker identification methods
Gaussian mixture model-universal background model classification technique
performance
response
speech signals
text-dependent speaker databases
2016-07-08 16:59:16
Figure
https://plos.figshare.com/articles/figure/Text-dependent_speaker_identification_performance_of_the_proposed_and_existing_methods_using_the_UM_database_/3908937
<p>Two cases of frequency bands were considered: left panels show the performance of the SI systems using features from the narrowband frequencies (<1 kHz), and the right panels represent performances for the wideband frequencies. Results are shown as a function of SNR with three different types of noise (A: white Gaussian noise, B: pink noise, and C: street noise). Speech samples from 39 speakers were used for evaluation and comparison of the performance of different methods.</p>