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WIESNER, M.; LIU, C.; ONDEL YANG, L.; HARMAN, C.; MANOHAR, V.; TRMAL, J.; HUANG, Z.; DEHAK, N.; KHUDANPUR, S.
Original Title
Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
Automatic speech recognition (ASR) systems often need to bedeveloped for extremely low-resource languages to serve endusessuch as audio content categorization and search. Whileuniversal phone recognition is natural to consider when no transcribedspeech is available to train an ASR system in a language,adapting universal phone models using very small amounts(minutes rather than hours) of transcribed speech also needs tobe studied, particularly with state-of-the-art DNN-based acousticmodels. The DARPA LORELEI program provides a frameworkfor such very-low-resource ASR studies, and provides anextrinsic metric for evaluating ASR performance in a humanitarianassistance, disaster relief setting. This paper presentsour Kaldi-based systems for the program, which employ a universalphone modeling approach to ASR, and describes recipesfor very rapid adaptation of this universal ASR system. Theresults we obtain significantly outperform results obtained bymany competing approaches on the NIST LoReHLT 2017 Evaluationdatasets
English abstract
Keywords
Universal acoustic models, topic identification,cross-language information retrieval, transfer learning, lowresourcespeech recognition
Key words in English
Authors
RIV year
2020
Released
02.09.2018
Publisher
International Speech Communication Association
Location
Hyderabad
Book
Proceedings of Interspeech
ISBN
1990-9772
Periodical
Volume
2018
Number
9
State
French Republic
Pages from
2052
Pages to
2056
Pages count
5
URL
https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1836.html
BibTex
@inproceedings{BUT163405, author="WIESNER, M. and LIU, C. and ONDEL YANG, L. and HARMAN, C. and MANOHAR, V. and TRMAL, J. and HUANG, Z. and DEHAK, N. and KHUDANPUR, S.", title="Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages", booktitle="Proceedings of Interspeech", year="2018", journal="Proceedings of Interspeech", volume="2018", number="9", pages="2052--2056", publisher="International Speech Communication Association", address="Hyderabad", doi="10.21437/Interspeech.2018-1836", issn="1990-9772", url="https://www.isca-speech.org/archive/Interspeech_2018/abstracts/1836.html" }
Documents
wiesner_interspeech2018_1836