Graduate School and Research Center in Digital Sciences

Open-set semi-supervised audio-visual speaker recognition using co-training LDA and sparse representation classifiers

Zhao, Xuran; Evans, Nicholas; Dugelay, Jean-Luc

ICASSP 2013, 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, May 26-31, Vancouver, Canada

Semi-supervised learning is attracting growing interest within the biometrics community. Almost all prior work focuses on closedset scenarios, in which samples labelled automatically are assumed to belong to an enrolled class. This is often not the case in realistic applications and thus open-set alternatives are needed. This paper proposes a new approach to open-set, semi-supervised learning based on co-training, Linear Discriminant Analysis (LDA) subspaces and Sparse Representation Classifiers (SRCs). Experiments on the standard MOBIO dataset show how the new approach can utilize automatically labelled data to augment a smaller, manually labelled dataset and thus improve the performance of an open-set audio-visual person recognition system.

Document Doi Bibtex

Title:Open-set semi-supervised audio-visual speaker recognition using co-training LDA and sparse representation classifiers
Keywords:Semi-supervised learning, open-set identification, multimodal biometrics, co-training
Type:Conference
Language:English
City:Vancouver
Country:CANADA
Date:
Department:Digital Security
Eurecom ref:4016
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Bibtex: @inproceedings{EURECOM+4016, doi = {http://dx.doi.org/10.1109/ICASSP.2013.6638208}, year = {2013}, title = {{O}pen-set semi-supervised audio-visual speaker recognition using co-training {LDA} and sparse representation classifiers}, author = {{Z}hao, {X}uran and {E}vans, {N}icholas and {D}ugelay, {J}ean-{L}uc}, booktitle = {{ICASSP} 2013, 38th {IEEE} {I}nternational {C}onference on {A}coustics, {S}peech, and {S}ignal {P}rocessing, {M}ay 26-31, {V}ancouver, {C}anada}, address = {{V}ancouver, {CANADA}}, month = {05}, url = {http://www.eurecom.fr/publication/4016} }
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