Abstract
This paper looks at the extraction of nonlinear turbulence information of the speech signal using Multi-Scale Fractal Dimension (MFD) as the additional feature to the convectional MFCC features. The MFD is estimated using both Box-Counting and Minkowiski-Bouligand dimensions. The proposed framework uses this feature extraction together with sub-band based speaker modeling rather than the wide-band approach. Results showed that the proposed framework with Box-Counting feature extraction improves the performance of the classical wideband approach with up 10% identification rate.
| Original language | English |
|---|---|
| Pages (from-to) | 1152-1157 |
| Number of pages | 6 |
| Journal | WSEAS Transactions on Systems |
| Volume | 5 |
| Issue number | 5 |
| Publication status | Published - May 2006 |
| Externally published | Yes |
Keywords
- Box-counting
- Minkowiski-Bouligand
- Multi-scale fractal dimension
- Speaker identification
ASJC Scopus subject areas
- Control and Systems Engineering
- Computer Science Applications