%Aigaion2 BibTeX export from Idiap Publications
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@TECHREPORT{Asaei_Idiap-RR-11-2010,
         author = {Asaei, Afsaneh and Bourlard, Herv{\'{e}} and Picart, Benjamin},
       projects = {Idiap},
          month = {6},
          title = {Investigation of kNN Classifier on Posterior Features Towards Application in Automatic Speech Recognition},
           type = {Idiap-RR},
         number = {Idiap-RR-11-2010},
           year = {2010},
    institution = {Idiap},
       abstract = {Class posterior distributions can be used to classify or as intermediate features, which can be further
exploited in different classifiers (e.g., Gaussian Mixture Models, GMM) towards improving speech
recognition performance. In this paper we examine the possibility to use kNN classifier to perform
local phonetic classification of class posterior distribution extracted from acoustic vectors. In that
framework, we also propose and evaluate a new kNN metric based on the relative angle between
feature vectors to define the nearest neighbors. This idea is inspired by the orthogonality
characteristic of the posterior features. To fully exploit this attribute, kNN is used in two main steps:
(1) the distance is computed as the cosine function of the relative angle between the test vector and
the training vector and (2) the nearest neighbors are defined as the samples within a specific relative
angle to the test data and the test samples which do not have enough labels in such a hyper-cone are
considered as uncertainties and left undecided. This approach is evaluated on TIMIT database and
compared to other metrics already used in literature for measuring the similarity between posterior
probabilities. Based on our experiments, the proposed approach yield 78.48\% frame level accuracy
while specifying 15.17\% uncertainties in the feature space.},
            pdf = {https://publications.idiap.ch/attachments/reports/2009/Asaei_Idiap-RR-11-2010.pdf}
}