Mobile phone speaker recognition
Today in a society with well-developed technology, a high penetration rate of mobile devices clearly illustrated how well-woven our portable phones are, into our lives. Therefore, security and privacy is a growing issue. To solve this, biometric systems such as speaker recognition can be used to inc...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
2013
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/55031 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-55031 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-550312023-03-03T20:53:41Z Mobile phone speaker recognition Thang, Hui Ru. Chng Eng Siong School of Computer Engineering DRNTU::Engineering::Computer science and engineering Today in a society with well-developed technology, a high penetration rate of mobile devices clearly illustrated how well-woven our portable phones are, into our lives. Therefore, security and privacy is a growing issue. To solve this, biometric systems such as speaker recognition can be used to increase protection level and security. The purpose of this project is to implement a speaker recognition system in Android platform, on S3. The system should allow users to record his speech utterance, extract MFCC features and test it against the speaker’s model. If the utterance is from the speaker, the system should accept it and reject if otherwise. Server-client architecture is used, where UBM is trained at server side and preloaded into android devices (client) in order to perform the verification task. Experimental results showed peak performance when 256 GMM was used. Above that, the problem of over-fitting occurs. More training data can be supplied and experiments can be extended to out-of set testing. Future enhancement includes storing the UBM online in a database to avoid manual transfer of UBM when large number of clients is used. The current verification system can also act as a baseline system and be compared with other systems created in the future, such as iVector and GMM-SVM. Bachelor of Engineering (Computer Science) 2013-12-04T01:38:44Z 2013-12-04T01:38:44Z 2013 2013 Final Year Project (FYP) http://hdl.handle.net/10356/55031 en Nanyang Technological University 54 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Computer science and engineering |
spellingShingle |
DRNTU::Engineering::Computer science and engineering Thang, Hui Ru. Mobile phone speaker recognition |
description |
Today in a society with well-developed technology, a high penetration rate of mobile devices clearly illustrated how well-woven our portable phones are, into our lives. Therefore, security and privacy is a growing issue. To solve this, biometric systems such as speaker recognition can be used to increase protection level and security. The purpose of this project is to implement a speaker recognition system in Android platform, on S3. The system should allow users to record his speech utterance, extract MFCC features and test it against the speaker’s model. If the utterance is from the speaker, the system should accept it and reject if otherwise. Server-client architecture is used, where UBM is trained at server side and preloaded into android devices (client) in order to perform the verification task. Experimental results showed peak performance when 256 GMM was used. Above that, the problem of over-fitting occurs. More training data can be supplied and experiments can be extended to out-of set testing. Future enhancement includes storing the UBM online in a database to avoid manual transfer of UBM when large number of clients is used. The current verification system can also act as a baseline system and be compared with other systems created in the future, such as iVector and GMM-SVM. |
author2 |
Chng Eng Siong |
author_facet |
Chng Eng Siong Thang, Hui Ru. |
format |
Final Year Project |
author |
Thang, Hui Ru. |
author_sort |
Thang, Hui Ru. |
title |
Mobile phone speaker recognition |
title_short |
Mobile phone speaker recognition |
title_full |
Mobile phone speaker recognition |
title_fullStr |
Mobile phone speaker recognition |
title_full_unstemmed |
Mobile phone speaker recognition |
title_sort |
mobile phone speaker recognition |
publishDate |
2013 |
url |
http://hdl.handle.net/10356/55031 |
_version_ |
1759855186591875072 |