Inter dataset variability compensation for speaker recognition

Recently satisfactory results have been obtained in NIST speaker recognition evaluations. These results are mainly due to accurate modeling of a very large development dataset provided by LDC. However, for many realistic scenarios the use of this development dataset is limited due to a dataset misma...

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Bibliographic Details
Published in2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 4002 - 4006
Main Author Aronowitz, Hagai
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2014
Subjects
Conferences
domain adaptation challenge
i-vector
inter dataset variability compensation
Mixers
NIST
robust speaker recognition
Robustness
Speaker recognition
Tin
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Summary:Recently satisfactory results have been obtained in NIST speaker recognition evaluations. These results are mainly due to accurate modeling of a very large development dataset provided by LDC. However, for many realistic scenarios the use of this development dataset is limited due to a dataset mismatch. In such cases, collection of a large enough dataset is infeasible. In this work we analyze the sources of degradation for a particular setup in the context of an i-vector PLDA system and conclude that the main source for degradation is an i-vector dataset shift. As a remedy, we introduce inter dataset variability compensation (IDVC) to explicitly compensate for dataset shift in the i-vector space. This is done using the nuisance attribute projection (NAP) method. Using IDVC we managed to reduce error dramatically by more than 50% for the domain mismatch setup.
ISSN:1520-6149
2379-190X
DOI:10.1109/ICASSP.2014.6854353