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Castan, D.; McLaren, M.; Ferrer, L.; Lawson, A.; Lozano-Diez, A.; Lacerda F.; Strombergsson S.; Wlodarczak M.; Heldner M.; Gustafson J.; House D. "Improving robustness of speaker recognition to new conditions using unlabeled data" (2017) 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017. 2017-August:3737-3741
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Abstract:

Unsupervised techniques for the adaptation of speaker recognition are important due to the problem of condition mismatch that is prevalent when applying speaker recognition technology to new conditions and the general scarcity of labeled 'in-domain' data. In the recent NIST 2016 Speaker Recognition Evaluation (SRE), symmetric score normalization (Snorm) and calibration using unlabeled in-domain data were shown to be beneficial. Because calibration requires speaker labels for training, speaker-clustering techniques were used to generate pseudo-speakers for learning calibration parameters in those cases where only unlabeled in-domain data was available. These methods performed well in the SRE16. It is unclear, however, whether those techniques generalize well to other data sources. In this work, we benchmark these approaches on several distinctly different databases, after we describe our SRI-CON-UAM team system submission for the NIST 2016 SRE. Our analysis shows that while the benefit of S-norm is also observed across other datasets, applying speaker-clustered calibration provides considerably greater benefit to the system in the context of new acoustic conditions. Copyright © 2017 ISCA.

Registro:

Documento: Conferencia
Título:Improving robustness of speaker recognition to new conditions using unlabeled data
Autor:Castan, D.; McLaren, M.; Ferrer, L.; Lawson, A.; Lozano-Diez, A.; Lacerda F.; Strombergsson S.; Wlodarczak M.; Heldner M.; Gustafson J.; House D.
Filiación:Speech Technology and Research Laboratory, SRI International, California, United States
Instituto de Investigación en Ciencias de la Computación (ICC), CONICET-UBA, Argentina
Audias-ATVS, Universidad Autonoma de Madrid, Madrid, Spain
Palabras clave:NIST SRE16; Score Calibration; Score Normalization; Trial-based Calibration; Calibration; Speech communication; Acoustic conditions; Calibration parameters; NIST SRE16; Score normalization; Speaker clustering; Speaker recognition; Speaker recognition evaluations; Unsupervised techniques; Speech recognition
Año:2017
Volumen:2017-August
Página de inicio:3737
Página de fin:3741
DOI: http://dx.doi.org/10.21437/Interspeech.2017-605
Título revista:18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017
Título revista abreviado:Proc. Annu. Conf. Int. Speech. Commun. Assoc., INTERSPEECH
ISSN:2308457X
Registro:https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_2308457X_v2017-August_n_p3737_Castan

Referencias:

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Citas:

---------- APA ----------
Castan, D., McLaren, M., Ferrer, L., Lawson, A., Lozano-Diez, A., Lacerda F., Strombergsson S.,..., House D. (2017) . Improving robustness of speaker recognition to new conditions using unlabeled data. 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, 2017-August, 3737-3741.
http://dx.doi.org/10.21437/Interspeech.2017-605
---------- CHICAGO ----------
Castan, D., McLaren, M., Ferrer, L., Lawson, A., Lozano-Diez, A., Lacerda F., et al. "Improving robustness of speaker recognition to new conditions using unlabeled data" . 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017 2017-August (2017) : 3737-3741.
http://dx.doi.org/10.21437/Interspeech.2017-605
---------- MLA ----------
Castan, D., McLaren, M., Ferrer, L., Lawson, A., Lozano-Diez, A., Lacerda F., et al. "Improving robustness of speaker recognition to new conditions using unlabeled data" . 18th Annual Conference of the International Speech Communication Association, INTERSPEECH 2017, vol. 2017-August, 2017, pp. 3737-3741.
http://dx.doi.org/10.21437/Interspeech.2017-605
---------- VANCOUVER ----------
Castan, D., McLaren, M., Ferrer, L., Lawson, A., Lozano-Diez, A., Lacerda F., et al. Improving robustness of speaker recognition to new conditions using unlabeled data. Proc. Annu. Conf. Int. Speech. Commun. Assoc., INTERSPEECH. 2017;2017-August:3737-3741.
http://dx.doi.org/10.21437/Interspeech.2017-605