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Feature learning based on nonnegative matrix factorisation for speaker identification, par Romain Serizel, 19/05/2016

Salle : B555

Horaire : 14:00

 

Abstract : The main target of speaker identification is to assert whether or not the speaker in an audio recording is known and if he/she is known, to find his/her identity. A recent trend is to use  feature learning based approaches to overcome the limitations of hand-craft features. This talk will review the dominant paradigm (the so-called I-vector approach) and will propose an alternative solution based on group nonnegative matrix factorisation (NMF). The scalability issue in NMF-based approaches will then be considered. Solutions taking advantage of stochastic mini-batch processing and general purpose graphical processing units (GPGPU) will be proposed, aiming at highly efficient feature learning for large scale datasets.

 

Slides : rserizel_TPT_19_05_2016

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