All

What are you looking for?

All
Projects
Results
Organizations

Quick search

  • Projects supported by TA ČR
  • Excellent projects
  • Projects with the highest public support
  • Current projects

Smart search

  • That is how I find a specific +word
  • That is how I leave the -word out of the results
  • “That is how I can find the whole phrase”

Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F20%3APU136482" target="_blank" >RIV/00216305:26230/20:PU136482 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/9053982" target="_blank" >https://ieeexplore.ieee.org/document/9053982</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP40776.2020.9053982" target="_blank" >10.1109/ICASSP40776.2020.9053982</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Optimizing Bayesian Hmm Based X-Vector Clustering for the Second Dihard Speech Diarization Challenge

  • Original language description

    This paper presents an analysis of our diarization system winning the second DIHARD speech diarization challenge, track 1. This system is based on clustering x-vector speaker embeddings extracted every 0.25s from short segments of the input recording. In this paper, we focus on the two x-vector clustering methods employed, namely Agglomerative Hierarchical Clustering followed by a clustering based on Bayesian Hidden Markov Model (BHMM). Even though the system submitted to the challenge had further post-processing steps, we will show that using this BHMM solely is enough to achieve the best performance in the challenge. The analysis will show improvements achieved by optimizing individual processing steps, including a simple procedure to effectively perform "domain adaptation" by Probabilistic Linear Discriminant Analysis model interpolation. All experiments are performed in the DIHARD II evaluation framework.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

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

Result continuities

  • Project

    <a href="/en/project/LQ1602" target="_blank" >LQ1602: IT4Innovations excellence in science</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

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

  • ISBN

    978-1-5090-6631-5

  • ISSN

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    6519-6523

  • Publisher name

    IEEE Signal Processing Society

  • Place of publication

    Barcelona

  • Event location

    Barcelona

  • Event date

    May 4, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000615970406156