Wavelet basis selection for enhanced speech parametrization in speaker verification

Iosif Mporas, Todor Ganchev, Mihalis Siafarikas, Tsenka Stoyanova

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


We study the inherent properties of nine wavelet functions and subsequently evaluate their applicability as basis functions in a speech parametrization scheme that is advantageous for speaker verification. Particularly, the inherent properties of nine candidate basis functions are initially analysed and their advantages and disadvantages are discussed. Subsequently, all candidates are employed in a well-proven speech parametrization scheme, and the resulting speech features are computed. Finally, these speech features are evaluated in a common experimental set-up on the speaker verification task. The experimental results, obtained on two well-known speaker recognition databases, show that the Battle-Lemarié wavelet function is the most advantageous one, among all other functions evaluated here, since it leads to the most beneficial speech descriptors. When compared to the baseline Mel-frequency cepstral coefficients (MFCC), a relative reduction of the equal error rate by 4.2 % was observed on the 2001 NIST speaker recognition evaluation database, and by 2.3 % on the Polycost speaker recognition database.
Original languageEnglish
Pages (from-to)27-36
Number of pages10
JournalInternational Journal of Speech Technology
Issue number1
Early online date16 Jun 2013
Publication statusPublished - 31 Mar 2014


Dive into the research topics of 'Wavelet basis selection for enhanced speech parametrization in speaker verification'. Together they form a unique fingerprint.

Cite this