Detail publikace

Score Fusion in Text-Dependent Speaker Recognition Systems

MEKYSKA, J. FAÚNDEZ ZANUY, M. SMÉKAL, Z. FABREGAS, J.

Originální název

Score Fusion in Text-Dependent Speaker Recognition Systems

Typ

článek v časopise - ostatní, Jost

Jazyk

angličtina

Originální abstrakt

According to some significant advantages, the text-dependent speaker recognition is still widely used in biometric systems. These systems are, in comparison with the text-independent, more accurate and resistant against the replay attacks. There are many approaches regarding the text-dependent recognition. This paper introduces a combination of classifiers based on fractional distances, biometric dispersion matcher and dynamic time warping. The first two mentioned classifiers are based on a voice imprint. They have low memory requirements while the recognition procedure is fast. This is advantageous especially in low-cost biometric systems supplied by batteries. It is shown that using the trained score fusion, it is possible to reach successful detection rate equal to 98.98 % and 92.19 % in case of microphone mismatch. During verification, system reached equal error rate 2.55 % and 6.77 % when assuming the microphone mismatch. System was tested using Catalan database which consists of 48 speakers (three 3 s training samples per speaker).

Klíčová slova

text-dependent speaker recognition, voice imprint, fractional distances, biometric dispersion matcher, dynamic time warping

Autoři

MEKYSKA, J.; FAÚNDEZ ZANUY, M.; SMÉKAL, Z.; FABREGAS, J.

Rok RIV

2011

Vydáno

24. 11. 2011

Nakladatel

Springer

ISSN

0302-9743

Periodikum

Lecture Notes in Computer Science

Ročník

6800

Číslo

12

Stát

Spolková republika Německo

Strany od

120

Strany do

132

Strany počet

13

BibTex

@article{BUT75059,
  author="Jiří {Mekyska} and Marcos {Faúndez Zanuy} and Zdeněk {Smékal} and Joan {Fabregas}",
  title="Score Fusion in Text-Dependent Speaker Recognition Systems",
  journal="Lecture Notes in Computer Science",
  year="2011",
  volume="6800",
  number="12",
  pages="120--132",
  issn="0302-9743"
}