Spoken term detection of zero-resource language using machine learning

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

Abstract

In this paper, we propose a spoken term detection method for detection of terms in zero-resource languages. The proposed method uses the classifier (the speech comparator) trained by a machine learning method combined with the dynamic time warping method. The advantage of the proposed method is that the classifier can be trained using a large language resource that is different from the target language. We exploited the random forest as a classifier, and carried out an experiment of the spoken term detection from Kaqchikel speech. As a result, the proposed method showed better detection performance compared with the method based on the Euclidean distance.

Original languageEnglish
Title of host publication2018 International Conference on Intelligent Information Technology, ICIIT 2018
PublisherAssociation for Computing Machinery
Pages45-49
Number of pages5
ISBN (Electronic)9781450363785
DOIs
Publication statusPublished - 2018 Feb 26
Event2018 International Conference on Intelligent Information Technology, ICIIT 2018 - Hanoi, Viet Nam
Duration: 2018 Feb 262018 Feb 28

Publication series

NameACM International Conference Proceeding Series

Other

Other2018 International Conference on Intelligent Information Technology, ICIIT 2018
CountryViet Nam
CityHanoi
Period18/2/2618/2/28

Keywords

  • Dynamic time warping
  • Kaqchikel
  • Random forest
  • Spoken term detection

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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  • Cite this

    Ito, A., & Koizumi, M. (2018). Spoken term detection of zero-resource language using machine learning. In 2018 International Conference on Intelligent Information Technology, ICIIT 2018 (pp. 45-49). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3193063.3193068