Probabilistic embeddings for speaker diarization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU136530" target="_blank" >RIV/00216305:26230/20:PU136530 - isvavai.cz</a>

Result on the web

<a href="https://www.isca-speech.org/archive/Odyssey_2020/abstracts/75.html" target="_blank" >https://www.isca-speech.org/archive/Odyssey_2020/abstracts/75.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Odyssey.2020-4" target="_blank" >10.21437/Odyssey.2020-4</a>

Result language

angličtina

Original language name

Probabilistic embeddings for speaker diarization

Original language description

Speaker embeddings (x-vectors) extracted from very short segments of speech have recently been shown to give competitive performance in speaker diarization. We generalize this recipe by extracting from each speech segment, in parallel with the x-vector, also a diagonal precision matrix, thus providing a path for the propagation of information about the quality of the speech segment into a PLDA scoring backend. These precisions quantify the uncertainty about what the values of the embeddings might have been if they had been extracted from high quality speech segments. The proposed probabilistic embeddings (x-vectors with precisions) are interfaced with the PLDA model by treating the x-vectors as hidden variables and marginalizing them out. We apply the proposed probabilistic embeddings as input to an agglomerative hierarchical clustering (AHC) algorithm to do diarization in the DIHARD19 evaluation set. We compute the full PLDA likelihood by the book for each clustering hypothesis that is considered by AHC. We do joint discriminative training of the PLDA parameters and of the probabilistic x-vector extractor. We demonstrate accuracy gains relative to a baseline AHC algorithm, applied to traditional xvectors (without uncertainty), and which uses averaging of binary log-likelihood-ratios, rather than by-the-book scoring.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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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

2020

Confidentiality

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

Article name in the collection

Proceedings of Odyssey 2020 The Speaker and Language Recognition Workshop

ISBN

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ISSN

2312-2846

e-ISSN

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Number of pages

8

Pages from-to

24-31

Publisher name

International Speech Communication Association

Place of publication

Tokyo

Event location

Tokyo

Event date

Nov 1, 2020

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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