Bayesian HMM based x-vector clustering - VBx

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APR34229" target="_blank" >RIV/00216305:26230/20:PR34229 - isvavai.cz</a>

Výsledek na webu

<a href="https://github.com/BUTSpeechFIT/VBx" target="_blank" >https://github.com/BUTSpeechFIT/VBx</a>

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian HMM based x-vector clustering - VBx

Popis výsledku v původním jazyce

Diarization is the task of determining the number of speakers and "who speaks when" in a recording. It is part of speech data mining. The proposed software contains a full implementation of a Bayesian approach to do speaker diarization using low-dimensional neural representation of speakers (x-vectors) in individual segments. It follows the Brno University of Technology recipe for the Second DIHARD Diarization Challenge Track 1, where BUT was the winner. It consists of computing filter-bank features, computing x-vectors, performing Agglomerative Hierarchical Clustering on x-vectors as a first step to produce an initialization, applying Variational Bayes HMM over x-vectors to produce the diarization output, and scoring the diarization output. The software is written in Python and released as open-source under Apache License.

Název v anglickém jazyce

Bayesian HMM based x-vector clustering - VBx

Popis výsledku anglicky

Diarization is the task of determining the number of speakers and "who speaks when" in a recording. It is part of speech data mining. The proposed software contains a full implementation of a Bayesian approach to do speaker diarization using low-dimensional neural representation of speakers (x-vectors) in individual segments. It follows the Brno University of Technology recipe for the Second DIHARD Diarization Challenge Track 1, where BUT was the winner. It consists of computing filter-bank features, computing x-vectors, performing Agglomerative Hierarchical Clustering on x-vectors as a first step to produce an initialization, applying Variational Bayes HMM over x-vectors to produce the diarization output, and scoring the diarization output. The software is written in Python and released as open-source under Apache License.

Druh

R - Software

CEP obor

—

OECD FORD obor

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

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2020

Kód důvěrnosti údajů

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

Interní identifikační kód produktu

x-vectors Diarization (aka VBx)

Technické parametry

https://www.fit.vut.cz/research/publication/12139/

Ekonomické parametry

Jedná se o opensource software.

IČO vlastníka výsledku

—

Název vlastníka

Fakulta informačních technologií