Discriminative Training of VBx Diarization

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F24%3APU152297" target="_blank" >RIV/00216305:26230/24:PU152297 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10446119" target="_blank" >https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10446119</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ICASSP48485.2024.10446119" target="_blank" >10.1109/ICASSP48485.2024.10446119</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Discriminative Training of VBx Diarization

Popis výsledku v původním jazyce

Bayesian HMM clustering of x-vector sequences (VBx) has be- come a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a gen- eratively trained probabilistic linear discriminant analysis (PLDA) for speaker distribution modeling, and Bayesian inference to esti- mate the assignment of x-vectors to speakers. This paper presents a new framework for updating the VBx parameters using discrim- inative training, which directly optimizes a predefined loss. We also propose a new loss that better correlates with the diarization error rate compared to binary cross-entropy - the default choice for diarization end-to-end systems. Proof-of-concept results across three datasets (AMI, CALLHOME, and DIHARD II) demonstrate the method's capability of automatically finding hyperparameters, achieving comparable performance to those found by extensive grid search, which typically requires additional hyperparameter behavior knowledge. Moreover, we show that discriminative fine-tuning of PLDA can further improve the model's performance. We release the source code with this publication.

Název v anglickém jazyce

Discriminative Training of VBx Diarization

Popis výsledku anglicky

Bayesian HMM clustering of x-vector sequences (VBx) has be- come a widely adopted diarization baseline model in publications and challenges. It uses an HMM to model speaker turns, a gen- eratively trained probabilistic linear discriminant analysis (PLDA) for speaker distribution modeling, and Bayesian inference to esti- mate the assignment of x-vectors to speakers. This paper presents a new framework for updating the VBx parameters using discrim- inative training, which directly optimizes a predefined loss. We also propose a new loss that better correlates with the diarization error rate compared to binary cross-entropy - the default choice for diarization end-to-end systems. Proof-of-concept results across three datasets (AMI, CALLHOME, and DIHARD II) demonstrate the method's capability of automatically finding hyperparameters, achieving comparable performance to those found by extensive grid search, which typically requires additional hyperparameter behavior knowledge. Moreover, we show that discriminative fine-tuning of PLDA can further improve the model's performance. We release the source code with this publication.

Druh

D - Stať ve sborníku

CEP obor

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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

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

ISBN

979-8-3503-4485-1

ISSN

—

e-ISSN

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Počet stran výsledku

5

Strana od-do

11871-11875

Název nakladatele

IEEE Signal Processing Society

Místo vydání

Seoul

Místo konání akce

Seoul

Datum konání akce

14. 4. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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