Cover Image

Neural response based speaker identification under noisy condition / Leyla Roohisefat

Speaker identification is the mechanism of determining a person among a set of speakers to certify whether that person is who he claims to be. The available speaker identification systems are mostly based on the acoustical signal itself. The problem is that they are very sensitive to noise and ca...

Full description

Saved in:
Bibliographic Details
Main Author: Leyla, Roohisefat
Format: Thesis
Published: 2014
Subjects:
T Technology (General)
Online Access:http://studentsrepo.um.edu.my/7782/1/Leyla_Roohisefat_final_thesis.pdf
http://studentsrepo.um.edu.my/7782/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.um.stud.7782
record_format eprints
spelling my.um.stud.77822017-10-25T08:37:38Z Neural response based speaker identification under noisy condition / Leyla Roohisefat Leyla, Roohisefat T Technology (General) Speaker identification is the mechanism of determining a person among a set of speakers to certify whether that person is who he claims to be. The available speaker identification systems are mostly based on the acoustical signal itself. The problem is that they are very sensitive to noise and can work only at very high signal-to-noise ratio (SNR). However, neural responses are very robust against background noise. In this study, a wellknown model of the auditory periphery by Zilany and colleagues (J. Acous. Soc. Am., 2009) is employed to simulate the neural responses, known as neurogram, on identifying a speaker, and then average discharge rate or envelope (ENV) and the temporal fine structure (TFS) are computed from the neurogram. The resulted vectors are used to train the system by employing two types of classifiers, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). The database consists of text-dependent speech samples from 39 speakers, and 10 speech samples were recorded for each speaker in a quiet room. The performance of the proposed method is compared with the traditional acoustic feature (mel-frequency-cepstral-coefficient, MFCC) based speaker identification method for both under quiet and noisy conditions. As the neural responses are robust to noise, the proposed neural response based system using TFS responses performs better than MFCC-based method, especially under noisy conditions. In general, GMM shows better accuracy for the proposed method than using HMM as a classifier. 2014 Thesis NonPeerReviewed application/pdf http://studentsrepo.um.edu.my/7782/1/Leyla_Roohisefat_final_thesis.pdf Leyla, Roohisefat (2014) Neural response based speaker identification under noisy condition / Leyla Roohisefat. Masters thesis, University of Malaya. http://studentsrepo.um.edu.my/7782/
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Student Repository
url_provider http://studentsrepo.um.edu.my/
topic T Technology (General)
spellingShingle T Technology (General)
Leyla, Roohisefat
Neural response based speaker identification under noisy condition / Leyla Roohisefat
description Speaker identification is the mechanism of determining a person among a set of speakers to certify whether that person is who he claims to be. The available speaker identification systems are mostly based on the acoustical signal itself. The problem is that they are very sensitive to noise and can work only at very high signal-to-noise ratio (SNR). However, neural responses are very robust against background noise. In this study, a wellknown model of the auditory periphery by Zilany and colleagues (J. Acous. Soc. Am., 2009) is employed to simulate the neural responses, known as neurogram, on identifying a speaker, and then average discharge rate or envelope (ENV) and the temporal fine structure (TFS) are computed from the neurogram. The resulted vectors are used to train the system by employing two types of classifiers, Gaussian Mixture Model (GMM) and Hidden Markov Model (HMM). The database consists of text-dependent speech samples from 39 speakers, and 10 speech samples were recorded for each speaker in a quiet room. The performance of the proposed method is compared with the traditional acoustic feature (mel-frequency-cepstral-coefficient, MFCC) based speaker identification method for both under quiet and noisy conditions. As the neural responses are robust to noise, the proposed neural response based system using TFS responses performs better than MFCC-based method, especially under noisy conditions. In general, GMM shows better accuracy for the proposed method than using HMM as a classifier.
format Thesis
author Leyla, Roohisefat
author_facet Leyla, Roohisefat
author_sort Leyla, Roohisefat
title Neural response based speaker identification under noisy condition / Leyla Roohisefat
title_short Neural response based speaker identification under noisy condition / Leyla Roohisefat
title_full Neural response based speaker identification under noisy condition / Leyla Roohisefat
title_fullStr Neural response based speaker identification under noisy condition / Leyla Roohisefat
title_full_unstemmed Neural response based speaker identification under noisy condition / Leyla Roohisefat
title_sort neural response based speaker identification under noisy condition / leyla roohisefat
publishDate 2014
url http://studentsrepo.um.edu.my/7782/1/Leyla_Roohisefat_final_thesis.pdf
http://studentsrepo.um.edu.my/7782/
_version_ 1738506062428897280
score 13.211869

Similar Items