Text-independent MFCCs vectors classification improvement using local ICA

Abdenebi Rouigueb, Salim Chitroub, Ahmed Bouridane

Research output: Contribution to journalArticlepeer-review

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.
Original languageEnglish
Pages (from-to)1-6
JournalIEEE International Workshop on Machine Learning for Signal Processing, MLSP
DOIs
Publication statusPublished - 2013
Event2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) - Southampton
Duration: 1 Jan 2013 → …

Fingerprint Dive into the research topics of 'Text-independent MFCCs vectors classification improvement using local ICA'. Together they form a unique fingerprint.

Cite this