  This paper presents an exhaustive study about the robustness of several
parameterizations, in speaker verification and identification tasks. We have
studied several mismatch conditions: different recording sessions, microphones,
and different languages (it has been obtained from a bilingual set of
speakers). This study reveals that the combination of several parameterizations
can improve the robustness in all the scenarios for both tasks, identification
and verification. In addition, two different methods have been evaluated:
vector quantization, and covariance matrices with an arithmetic-harmonic
sphericity measure.
