Astuti, Yenni and Hidayat, Risanuri and Bejo, Agus (2021) Feature Extraction using Gaussian-MFCC for Speaker Recognition System. In: 2021 IEEE 5th International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 24-25 November 2021, Purwokerto.
Full text not available from this repository. (Request a copy)Abstract
Voice is one of biometrics that is interesting to be analyzed. A voice from a person has a unique form. It cannot be identically produced twice or more. To extract this uniqueness, feature extraction is needed. One of the popular feature extractions is MFCC. The original MFCC uses triangular filter as its filter-bank. In this paper, the original filter-bank is compared with Gaussian filter-bank to obtain a better recognition output. For the decision, Euclidean distance and Manhattan distance are used. In this paper, the result shows that Gaussian filter-bank can substitute the original filter-bank of MFCC to reach a better result. © 2021 IEEE.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Additional Information: | Cited by: 2 |
| Uncontrolled Keywords: | Extraction; Feature extraction; Gaussian distribution; Speech recognition; Euclidean distance; Features extraction; Filters bank; Gaussian filter-bank; Gaussian filters; Gaussians; Manhattan distance; Mfcc; Speaker recognition; Speaker recognition system; Filter banks |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Electrical and Information Technology Department |
| Depositing User: | Sri JUNANDI |
| Date Deposited: | 25 Oct 2024 01:58 |
| Last Modified: | 25 Oct 2024 01:58 |
| URI: | https://ir.lib.ugm.ac.id/id/eprint/8609 |
Actions (login required)
- View Item