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Supervised Group Nonnegative Matrix Factorisation With Similarity Constraints And Applications To Speaker Identification
Abstract : This paper presents supervised feature learning approaches for speaker identification that rely on nonnegative matrix factorisa-tion. 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. Index Terms— Nonnegative matrix factorisation, feature learning , dictionary learning, online learning, speaker identification
Cited literature [27 references]
https://hal.inria.fr/hal-01484744
Contributor : Romain Serizel <>
Submitted on : Tuesday, March 7, 2017 - 4:25:06 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:02 PM
Document(s) archivé(s) le : Thursday, June 8, 2017 - 2:22:38 PM
Contributor : Romain Serizel <>
Submitted on : Tuesday, March 7, 2017 - 4:25:06 PM
Last modification on : Wednesday, June 24, 2020 - 4:19:02 PM
Document(s) archivé(s) le : Thursday, June 8, 2017 - 2:22:38 PM
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