TY - GEN
T1 - Spoken term detection based on acoustic models trained in multiple languages for zero-resource language
AU - Mizuochi, Satoru
AU - Chiba, Yuya
AU - Nose, Takashi
AU - Ito, Akinori
N1 - Funding Information:
A part of this work was supported by JSPS Grant-in-Aid for Scientific Research 19H05589.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/13
Y1 - 2020/10/13
N2 - 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.
AB - 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.
KW - multiple languages
KW - posteriorgram
KW - spoken term detection
KW - zero-resource language
UR - http://www.scopus.com/inward/record.url?scp=85099391601&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85099391601&partnerID=8YFLogxK
U2 - 10.1109/GCCE50665.2020.9291761
DO - 10.1109/GCCE50665.2020.9291761
M3 - Conference contribution
AN - SCOPUS:85099391601
T3 - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
SP - 351
EP - 352
BT - 2020 IEEE 9th Global Conference on Consumer Electronics, GCCE 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE Global Conference on Consumer Electronics, GCCE 2020
Y2 - 13 October 2020 through 16 October 2020
ER -