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”

Convolutional Neural Network for speaker change detection in telephone speaker diarization system

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F17%3A43932069" target="_blank" >RIV/49777513:23520/17:43932069 - isvavai.cz</a>

  • Result on the web

    <a href="http://ieeexplore.ieee.org/document/7953097/" target="_blank" >http://ieeexplore.ieee.org/document/7953097/</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICASSP.2017.7953097" target="_blank" >10.1109/ICASSP.2017.7953097</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Convolutional Neural Network for speaker change detection in telephone speaker diarization system

  • Original language description

    The aim of this paper is to propose a speaker change detection technique based on Convolutional Neural Network (CNN) and evaluate its contribution to the performance of a speaker diarization system for telephone conversations. For the comparison we used an i-vector based speaker diarization system. The baseline speaker change detection uses Generalized Likelihood Ratio (GLR) metric. Experiments were conducted on the English part of the CallHome corpus. Our proposed CNN speaker change detection outperformed the GLR approach, reducing the Equal Error Rate relatively by 46 %. The final results on speaker diarization system indicate that the use of speaker change detection based on CNN is beneficial with relative improvement of diarization error rate by 28 %.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    20205 - Automation and control systems

Result continuities

  • Project

    <a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

  • Continuities

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

Others

  • Publication year

    2017

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on

  • ISBN

    978-1-5090-4117-6

  • ISSN

    1520-6149

  • e-ISSN

    neuvedeno

  • Number of pages

    5

  • Pages from-to

    4945-4949

  • Publisher name

    IEEE Signal Processing Society

  • Place of publication

    New York

  • Event location

    New Orleans, LA, USA

  • Event date

    Mar 5, 2017

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000414286205021