Employment of Subspace Gaussian Mixture Models in Speaker Recognition

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F15%3APU117038" target="_blank" >RIV/00216305:26230/15:PU117038 - isvavai.cz</a>

Result on the web

<a href="https://ieeexplore.ieee.org/document/7178811" target="_blank" >https://ieeexplore.ieee.org/document/7178811</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP.2015.7178811" target="_blank" >10.1109/ICASSP.2015.7178811</a>

Result language

angličtina

Original language name

Employment of Subspace Gaussian Mixture Models in Speaker Recognition

Original language description

This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significantly outperform traditional HMM/GMMs in Automatic Speech Recognition (ASR) applications. An extension to the basic SGMM framework allows to robustly estimate low-dimensional speaker vectors and exploit them for speaker adaptation. We propose a speaker verification framework based on low-dimensional speaker vectors estimated using SGMMs, trained in ASR manner using manual transcriptions. To test the robustness of the system, we evaluate the proposed approach with respect to the state-of-the-art i-vector extractor on the NIST SRE 2010 evaluation set and on four different length-utterance conditions: 3sec-10sec, 10 sec-30 sec, 30 sec-60 sec and full (untruncated) utterances. Experimental results reveal that while i-vector system performs better on truncated 3sec to 10sec and 10 sec to 30 sec utterances, noticeable improvements are observed with SGMMs especially on full length-utterance durations. Eventually, the proposed SGMM approach exhibits complementary properties and can thus be efficiently fused with i-vector based speaker verification system.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2015

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Proceedings of 2015 IEEE International Conference on Acoustics, Speech and Signal Processing

ISBN

978-1-4673-6997-8

ISSN

—

e-ISSN

—

Number of pages

5

Pages from-to

4445-4449

Publisher name

IEEE Signal Processing Society

Place of publication

South Brisbane, Queensland

Event location

Brisbane

Event date

Apr 19, 2015

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

000427402904111