Speaker feature modeling utilizing constrained maximum likelihood linear regression and Gaussian mixture models
This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker adaptation, while using Gaussian mixture models (GMM) to model the speaker and background models. For the input acoustic signals, the cepstral...
Saved in:
| Main Author: | |
|---|---|
| Format: | text |
| Published: |
Animo Repository
2020
|
| Subjects: | |
| Online Access: | https://animorepository.dlsu.edu.ph/faculty_research/2974 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | De La Salle University |
| Summary: | This paper describes a speaker recognition system based on feature extraction utilizing the constrained maximum likelihood linear regression (CMLLR) speaker adaptation, while using Gaussian mixture models (GMM) to model the speaker and background models. For the input acoustic signals, the cepstral features are derived to highlight the differences between test and training utterances. The CLSU dataset is used to test the efficiency and performance of the proposed CMLLR, Support Vector Machine, and GMM methods for modeling the speaker’s voice by characterizing the speaker features. © 2020, World Academy of Research in Science and Engineering. All rights reserved. |
|---|