Abstract
In this paper, a language identification system is described that implements the Fishervoice approach in order to reduce the dimensionality of the data. Fishervoice performs two-dimensional Principal Component Analysis (2D-PCA) and Linear Discriminant Analysis (LDA) to project the data into a discriminative subspace. After this transformation the speech utterances are transformed into supervectors and classified by means of a Support Vector Machine (SVM). Experiments performed on KALAKA-2 database, which includes speech in Spanish, Catalan, English, Basque, Galician and Portuguese, show that the Fishervoice-SVM system achieves good identification results while reducing dramatically the number of features needed to represent the speech utterances.
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Lopez-Otero, P., Docio-Fernandez, L., Garcia-Mateo, C. (2012). A Fishervoice-SVM Language Identification System. In: Caseli, H., Villavicencio, A., Teixeira, A., Perdigão, F. (eds) Computational Processing of the Portuguese Language. PROPOR 2012. Lecture Notes in Computer Science(), vol 7243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28885-2_43
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DOI: https://doi.org/10.1007/978-3-642-28885-2_43
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