<p>Automatic speaker recognition is an important key to speaker identification in media forensics and with the increase of cultures mixing, there?s an increase in bilingual speakers all around the world. The purpose of this thesis is to compare text-independent samples of one person using two different languages, Arabic and English, against a single language reference population. The hope is that a design can be started that may be useful in further developing software that can complete accurate text-independent ASR for bilingual speakers speaking either language against a single language reference population. This thesis took an Arabic model sample and compared it against samples that were both Arabic and English using and an Arabic reference population, all collected from videos downloaded from the Internet. All of the samples were text-independent and enhanced to optimal performance. The data was run through a biometric software called BATVOX 4.1, which utilizes the MFCCs and GMM methods of speaker recognition and identification. The result of testing through BATVOX 4.1 was likelihood ratios for each sample that were evaluated for similarities and differences, trends, and problems that had occurred.
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1605087 |
| Date | 16 December 2015 |
| Creators | Alamri, Safi S. |
| Publisher | University of Colorado at Denver |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
Page generated in 0.0019 seconds