Obiettivo 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. Campo scientifico natural sciencescomputer and information sciencesinternetnatural sciencesmathematicsapplied mathematicsstatistics and probabilitybayesian statisticsnatural sciencescomputer and information sciencesdata sciencedata miningnatural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learningnatural sciencescomputer and information sciencesartificial intelligencecomputational intelligence Programma(i) H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions Main Programme H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility Argomento(i) MSCA-IF-2016 - Individual Fellowships Invito a presentare proposte H2020-MSCA-IF-2016 Vedi altri progetti per questo bando Meccanismo di finanziamento MSCA-IF-EF-ST - Standard EF Coordinatore VYSOKE UCENI TECHNICKE V BRNE Contribution nette de l'UE € 142 720,80 Indirizzo ANTONINSKA 548/1 601 90 Brno Stred Cechia Mostra sulla mappa Regione Česko Jihovýchod Jihomoravský kraj Tipo di attività Higher or Secondary Education Establishments Collegamenti Contatta l’organizzazione Opens in new window Sito web Opens in new window Partecipazione a programmi di R&I dell'UE Opens in new window Rete di collaborazione HORIZON Opens in new window Costo totale € 142 720,80