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Supervised group nonnegative matrix factorisation with similarity constraints and applications to speaker identification

Citation Author(s):
victor bisot, slim essid, gaël richard
Submitted by:
romain serizel
Last updated:
1 March 2017 - 4:34am
Document Type:
Presentation Slides
Document Year:
2017
Event:
Presenters:
Romain Serizel
Paper Code:
AASP-L2.2
 

This paper presents supervised feature learning approaches for speaker identification that rely on nonnegative matrix factorisation. Recent studies have shown that group nonnegative matrix factorisation and task-driven supervised dictionary learning can help performing effective feature learning for audio classification problems.

This paper proposes to integrate a recent method that relies on group nonnegative matrix factorisation into a task-driven supervised framework for speaker identification. The goal is to capture both the speaker variability and the session variability while exploiting the
discriminative learning aspect of the task-driven approach. Results on a subset of the ESTER corpus prove that the proposed approach can be competitive with I-vectors.

Code available at https://github.com/rserizel/TGNMF

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