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”

From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F22%3APU146059" target="_blank" >RIV/00216305:26230/22:PU146059 - isvavai.cz</a>

  • Result on the web

    <a href="https://www.isca-speech.org/archive/pdfs/interspeech_2022/landini22_interspeech.pdf" target="_blank" >https://www.isca-speech.org/archive/pdfs/interspeech_2022/landini22_interspeech.pdf</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.21437/Interspeech.2022-10451" target="_blank" >10.21437/Interspeech.2022-10451</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    From Simulated Mixtures to Simulated Conversations as Training Data for End-to-End Neural Diarization

  • Original language description

    End-to-end neural diarization (EEND) is nowadays one of the most prominent research topics in speaker diarization. EEND presents an attractive alternative to standard cascaded diarization systems since a single system is trained at once to deal with the whole diarization problem. Several EEND variants and approaches are being proposed, however, all these models require large amounts of annotated data for training but available annotated data are scarce. Thus, EEND works have used mostly simulated mixtures for training. However, simulated mixtures do not resemble real conversations in many aspects. In this work we present an alternative method for creating synthetic conversations that resemble real ones by using statistics about distributions of pauses and overlaps estimated on genuine conversations. Furthermore, we analyze the effect of the source of the statistics, different augmentations and amounts of data. We demonstrate that our approach performs substantially better than the original one, while reducing the dependence on the fine-tuning stage. Experiments are carried out on 2-speaker telephone conversations of Callhome and DIHARD 3. Together with this publication, we release our implementations of EEND and the method for creating simulated conversations.

  • 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

    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)

Others

  • Publication year

    2022

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

  • ISBN

  • ISSN

    1990-9772

  • e-ISSN

  • Number of pages

    5

  • Pages from-to

    5095-5099

  • Publisher name

    International Speech Communication Association

  • Place of publication

    Incheon

  • Event location

    Incheon Korea

  • Event date

    Sep 18, 2022

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article