Phonotactic models based on bags of n-grams representations and discriminative classifiers are a popular approach to the language recognition problem. However, the large size of n-gram count vectors brings about some difficulties in discriminative classifiers. The subspace Multinomial model was recently proposed to effectively represent information contained in the n-grams using low-dimensional iVectors. The availability of a low-dimensional feature vector allows investigating different post-processing techniques and different classifiers to improve recognition performance. In this work, we analyze a set of discriminative classifiers based on Support Vector Machines and Logistic Regression and we propose an iVector post-processing technique which allows to improve recognition performance. The proposed systems are evaluated on the NIST LRE 2009 task.

Discriminative Classifiers for Phonotactic Language Recognition with iVectors / Soufifar, M.; Cumani, Sandro; Burget, L.; Černocký, J.. - (2012), pp. 4853-4856. ((Intervento presentato al convegno The 37th International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2012).

Discriminative Classifiers for Phonotactic Language Recognition with iVectors

CUMANI, SANDRO;
2012

Abstract

Phonotactic models based on bags of n-grams representations and discriminative classifiers are a popular approach to the language recognition problem. However, the large size of n-gram count vectors brings about some difficulties in discriminative classifiers. The subspace Multinomial model was recently proposed to effectively represent information contained in the n-grams using low-dimensional iVectors. The availability of a low-dimensional feature vector allows investigating different post-processing techniques and different classifiers to improve recognition performance. In this work, we analyze a set of discriminative classifiers based on Support Vector Machines and Logistic Regression and we propose an iVector post-processing technique which allows to improve recognition performance. The proposed systems are evaluated on the NIST LRE 2009 task.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2496927
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