Name
Abstract | ||
|---|---|---|
We propose art effective voice-based gender identification method using a support vector machine (SVM). The SVM is a binary classification algorithm that classifies two groups by finding the voluntary non-linear boundary in a feature space and is known to yield high classification performance. In the present work. we compare the identification performance of the SVM with that of a Gaussian mixture model (GMM)-based method Using the mel frequency cepstral coefficients (MFCC). A novel approach of incorporating a feature, fusion scheme based on it combination of the MFCC and the fundamental frequency is proposed with the aim of improving the performance of gender identification. Experimental results demonstrate that the gender identification performance using the SVM is significantly better than that of the GMM-based scheme. Moreover. the performance is substantially improved when the proposed features fusion technique is applied. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1093/ietcom/e91-b.10.3326 | IEICE TRANSACTIONS ON COMMUNICATIONS |
Keywords | DocType | Volume |
speech signal, gender identification, SVM, GMM, fundamental frequency | Journal | E91B |
Issue | ISSN | Citations |
10 | 0916-8516 | 5 |
PageRank | References | Authors |
0.60 | 6 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kye-hwan Lee | 1 | 19 | 2.92 |
| Sang-Ick Kang | 2 | 25 | 4.81 |
| Deok-Hwan Kim | 3 | 627 | 49.38 |
| joonhyuk | 4 | 136 | 26.87 |