Spoken term detection based on acoustic models trained in multiple languages for zero-resource language

Satoru Mizuochi, Yuya Chiba, Takashi Nose, Akinori Ito

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we study a spoken term detection method for zero-resource languages by using rich-resource languages. The examined method combines phonemic posteriorgrams (PPGs) extracted from phonemic classifiers of multiple languages and detects a query word based on dynamic time warping. As a result, the method showed better detection performance in a zero-resource language compared with the method using PPGs of a single language.

Original languageEnglish
Title of host publication2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages351-352
Number of pages2
ISBN (Electronic)9781728198026
DOIs
Publication statusPublished - 2020 Oct 13
Event9th IEEE Global Conference on Consumer Electronics, GCCE 2020 - Kobe, Japan
Duration: 2020 Oct 132020 Oct 16

Publication series

Name2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020

Conference

Conference9th IEEE Global Conference on Consumer Electronics, GCCE 2020
CountryJapan
CityKobe
Period20/10/1320/10/16

Keywords

  • multiple languages
  • posteriorgram
  • spoken term detection
  • zero-resource language

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering
  • Media Technology
  • Instrumentation
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition

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