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Speaker verification using adaptive dictionaries in non-negative spectrogram deconvolution

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Details

Original languageEnglish
Title of host publicationLatent Variable Analysis and Signal Separation
Subtitle of host publication12th International Conference, LVA/ICA 2015, Liberec, Czech Republic, August 25-28, 2015, Proceedings
PublisherSpringer Verlag
Pages462-469
Number of pages8
Volume9237
ISBN (Print)9783319224817
DOIs
Publication statusPublished - 2015
Publication typeA4 Article in a conference publication
EventInternational Conference on Latent Variable Analysis and Signal Separation -
Duration: 1 Jan 1900 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9237
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Latent Variable Analysis and Signal Separation
Period1/01/00 → …

Abstract

This article presents a new method for speaker verification, which is based on the non-negative matrix deconvolution (NMD) of the magnitude spectrogram of an observed utterance. In contrast to typical methods known from the literature, which are based on the assumption that the desired signal dominates (for example GMM-UBM, joint factor analysis, i-vectors), compositional models such as NMD describe a recording as a non-negative combination of latent components. The proposed model represents a spectrogram of a signal as a sum of spectrotemporal patterns that span durations of order about 150 ms, while many state of the art automatic speaker recognition systems model a probability distribution of features extracted from much shorter excerpts of speech signal (about 50 ms). Longer patterns carry information about dynamical aspects of modeled signal, for example information about accent and articulation. We use a parametric dictionary in the NMD and the parameters of the dictionary carry information about the speakers’ identity. The experiments performed on the CHiME corpus show that with the proposed approach achieves equal error rate comparable to an i-vector based system.

Publication forum classification

Field of science, Statistics Finland