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SPEAKER DICE SIGNED

Robust SPEAKER DIariazation systems using Bayesian inferenCE and deep learning methods

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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Project "SPEAKER DICE" data sheet

The following table provides information about the project.

Coordinator
VYSOKE UCENI TECHNICKE V BRNE 

Organization address
address: ANTONINSKA 548/1
city: BRNO STRED
postcode: 601 90
website: www.vutbr.cz

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Czech Republic [CZ]
 Project website http://www.fit.vutbr.cz/
 Total cost 142˙720 €
 EC max contribution 142˙720 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-03-01   to  2019-02-28

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    VYSOKE UCENI TECHNICKE V BRNE CZ (BRNO STRED) coordinator 142˙720.00

Map

 Project objective

The proposed project deals with Speaker Diarization (SD) which is commonly defined as the task of answering the question “who spoke when?” in a speech recording. The first objective of the proposal is to optimize the Bayesian approach to SD, which has shown to be promising for the tasks. For Variational Bayes (VB) inference, that is very sensitive to initialization, we will develop new fast ways of obtaining a good starting point. We will also explore alternative inference methods, such as collapsed VB or collapsed Gibbs Sampling, and investigate into alternative priors similar to those introduced for Bayesian speaker recognition models.

The second part of the proposal is motivated by the huge performance gains that, in recent years, have been brought to other recognition tasks by Deep Neural Networks (DNNs). In the context of SD, DNNs have been used in the computation of i-vectors, but their potential was never explored for other stages of SD. We will study ways of integrating DNNs in the different stages of SD systems.

The objectives of the proposal will be achieved by theoretical work, implementation, and careful testing on real speech data. The outcomes of the project are intended not only for scientific publications, but eagerly awaited by European speech data mining industry (for example Czech Phonexia or Spanish Agnitio).

The project is proposed by an excellent female researcher, Dr. Mireia Diez, having finished her thesis in the GTTS group of University of the Basque Country, one of the most important European labs dealing with speaker recognition and diarization. The proposed host is the Speech@FIT group of Brno University of Technology, with a 20-year track of top speech data mining research. The proposed research training and combination of skills of Dr. Diez and the host institution have chances to advance the state-of-the-art in speaker diarization, provide the applicant with improved career opportunities and benefit European industry.

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The information about "SPEAKER DICE" are provided by the European Opendata Portal: CORDIS opendata.

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