Eusipco 2011

At the end of August I have attended Eusipco 2011 conference in Barcelona, Spain. I have presented my work on speaker identification using diffusion maps, a manifold learning and dimensionality reduction method developed during the last years by Ronald Coifman and Stephane Lafon. The paper can be found here: “Speaker Identification Using Diffusion Maps”.
In this paper we propose a data-driven approach for speaker identification without assuming any particular speaker model. The goal in speaker identification task is to determine which one of a group of known speakers best matches a given voice sample. Here we focus on text-independent speaker identification, i.e. no assumption is made regarding the spoken text. Our approach is based on a recently developed manifold learning technique, named diffusion maps. Diffusion maps enable embedding of the recording into a new space, which is likely to capture the speech intrinsic structure. The algorithm was tested and compared to common identification algorithms, and our experiments had shown that the proposed algorithm obtains improved results when few labeled samples are available.

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