Text-independent MFCCs vectors classification improvement using local ICA

Rouigueb, Abdenebi, Chitroub, Salim and Bouridane, Ahmed (2013) Text-independent MFCCs vectors classification improvement using local ICA. IEEE International Workshop on Machine Learning for Signal Processing, MLSP. pp. 1-6. ISSN 2161-0363

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Abstract

In this paper, we propose a new classification scheme of MFCCs vectors in the context of speaker identification. The solution is built around the binary SVM classification between each speaker class and the background model class over the underlying spaces of the local independent components analysis using clustering. Experiments have been conducted on a sample of the MOBIO corpus.

Item Type:	Article
Uncontrolled Keywords:	background model, local independent component analysis, speaker recognition, text-independent
Subjects:	H600 Electronic and Electrical Engineering
Department:	Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User:	Becky Skoyles
Date Deposited:	19 Jan 2015 14:42
Last Modified:	13 Oct 2019 00:32
URI:	http://nrl.northumbria.ac.uk/id/eprint/20952

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