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Classification of Unvoiced Fricative Phonemes Using Geometric Methods


Michal Genussov 1 Yizhar Lavner 2 Israel Cohen 1
1 Department of Electrical Engineering, Technion- Israel Institute of Technology, Haifa, Israel
2 Department of Computer Science, Tel-Hai Academic College, Upper Galilee, Israel

Phoneme classification is the process of finding the phonetic identity of a short section of a spoken signal. Performances of existing classification techniques are often insufficient, since they rely on Euclidean distances between spectral and temporal features, whereas the relevant features lie in a non-linear manifold. In this work, we propose to integrate into the phoneme classification a non-linear manifold learning technique, namely "Diffusion maps". Diffusion maps builds a graph from the feature vectors and maps the connections in the graph to Euclidean distances, so using Euclidean distances for classification after the non-linear mapping is optimal. We show that Diffusion maps allows dimensionality reduction and improves the classification results.



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