Improving speaker identification rate using fractals

Fulufhelo V. Nelwamondo, Unathi Mahola, Tshilidzi Marwala

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

9 Citations (Scopus)


This paper reports on a text-dependent speaker identification system that combines Mel-frequency cepstral coefficients with non-linear turbulence information extracted using Multi-Scale Fractal Dimension (MFD). The MFD is estimated using Box-Counting and Minkowiski-Bouligand dimension. The proposed framework is implemented in conjunction with sub-band based speaker identification system. Results show that the proposed framework with Box-Counting feature extraction improves the performance of the classical wideband approach by up to 10% identification rate. It is further observed that the proposed framework gives the improved Bhattacharyya distance between impostors and speakers' speech distributions.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks 2006, IJCNN '06
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Print)0780394909, 9780780394902
Publication statusPublished - 2006
Externally publishedYes
EventInternational Joint Conference on Neural Networks 2006, IJCNN '06 - Vancouver, BC, Canada
Duration: 16 Jul 200621 Jul 2006

Publication series

NameIEEE International Conference on Neural Networks - Conference Proceedings
ISSN (Print)1098-7576


ConferenceInternational Joint Conference on Neural Networks 2006, IJCNN '06
CityVancouver, BC

ASJC Scopus subject areas

  • Software


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