Conference paper

GLEMBEK Ondřej, MA Jeff, MATĚJKA Pavel, ZHANG Bing, PLCHOT Oldřich, BURGET Lukáš and MATSOUKAS Spyros. Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms. In: Proceedings of ICASSP 2014. Florencie: IEEE Signal Processing Society, 2014, pp. 4060-4064. ISBN 978-1-4799-2892-7.
Publication language:english
Original title:Domain Adaptation Via Within-class Covariance Correction in I-Vector Based Speaker Recognition Systerms
Title (cs):Adaptace na doménu pomocí vnitro-třídní kovarianční opravy v systému pro rozpoznávání mluvčího založeném na i-vektorech
Proceedings:Proceedings of ICASSP 2014
Conference:The 39th International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
Place:Florencie, IT
Publisher:IEEE Signal Processing Society
speaker recognition, i-vectors, source normalization, LDA, inter-dataset variability compensation
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.
In this paper we propose a technique of Within-Class Covariance Correction (WCC) for Linear Discriminant Analysis (LDA) in Speaker Recognition to perform an unsupervised adaptation of LDA to an unseen data domain, and/or to compensate for speaker population difference among different portions of LDA training dataset. The paper follows on the study of source-normalization and interdatabase variability compensation techniques which deal with multimodal distribution of i-vectors. On the DARPA RATS (Robust Automatic Transcription of Speech) task, we show that, with two hours of unsupervised data, we improve the Equal-Error Rate (EER) by 17.5%, and 36% relative on the unmatched and semi-matched conditions, respectively. On the Domain Adaptation Challenge we show up to 70% relative EER reduction and we propose a data clustering procedure to identify the directions of the domain-based variability in the adaptation data.
   author = {Ond{\v{r}}ej Glembek and Jeff Ma and Pavel Mat{\v{e}}jka and
	Bing Zhang and Old{\v{r}}ich Plchot and Luk{\'{a}}{\v{s}}
	Burget and Spyros Matsoukas},
   title = {Domain Adaptation Via Within-class Covariance Correction in
	I-Vector Based Speaker Recognition Systerms},
   pages = {4060--4064},
   booktitle = {Proceedings of ICASSP 2014},
   year = {2014},
   location = {Florencie, IT},
   publisher = {IEEE Signal Processing Society},
   ISBN = {978-1-4799-2892-7},
   language = {english},
   url = {}

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