Detail publikace

Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms

GLEMBEK, O. MA, J. MATĚJKA, P. ZHANG, B. PLCHOT, O. BURGET, L. MATSOUKAS, S.

Originální název

Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms

Anglický název

Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms

Jazyk

en

Originální abstrakt

In this paper, we have shown a technique of within-class correction for Linear Discriminant Analysis estimation. We have shown that when correct dataset clustering is used, adapting the within-class covariance of LDA by low-rank between-dataset covariance matrix can lead to significant improvement of the system, namely up to 70% in the Domain Adaptation Task, and 17.5% and 36% relative in the RATS unmatched and semi-matched tasks, respectively. The dataset clustering problem gave us an interesting direction for future research.

Anglický abstrakt

In this paper, we have shown a technique of within-class correction for Linear Discriminant Analysis estimation. We have shown that when correct dataset clustering is used, adapting the within-class covariance of LDA by low-rank between-dataset covariance matrix can lead to significant improvement of the system, namely up to 70% in the Domain Adaptation Task, and 17.5% and 36% relative in the RATS unmatched and semi-matched tasks, respectively. The dataset clustering problem gave us an interesting direction for future research.

Dokumenty

BibTex


@inproceedings{BUT111543,
  author="Ondřej {Glembek} and Jeff {Ma} and Pavel {Matějka} and Bing {Zhang} and Oldřich {Plchot} and Lukáš {Burget} and Spyros {Matsoukas}",
  title="Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms",
  annote="In this paper, we have shown a technique of within-class correction for Linear
Discriminant Analysis estimation. We have shown that when correct dataset
clustering is used, adapting the within-class covariance of LDA by low-rank
between-dataset covariance matrix can lead to significant improvement of the
system, namely up to 70% in the Domain Adaptation Task, and 17.5% and 36%
relative in the RATS unmatched and semi-matched tasks, respectively. The dataset
clustering problem gave us an interesting direction for future research.",
  address="IEEE Signal Processing Society",
  booktitle="Proceedings of ICASSP 2014",
  chapter="111543",
  doi="10.1109/ICASSP.2014.6854359",
  edition="NEUVEDEN",
  howpublished="print",
  institution="IEEE Signal Processing Society",
  year="2014",
  month="may",
  pages="4060--4064",
  publisher="IEEE Signal Processing Society",
  type="conference paper"
}