Detail publikačního výsledku

Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages

WIESNER, M.; LIU, C.; ONDEL YANG, L.; HARMAN, C.; MANOHAR, V.; TRMAL, J.; HUANG, Z.; DEHAK, N.; KHUDANPUR, S.

Originální název

Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages

Anglický název

Automatic Speech Recognition and Topic Identification for Almost-Zero-Resource Languages

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

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

Anglický abstrakt

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

Klíčová slova

Universal acoustic models, topic identification,cross-language information retrieval, transfer learning, lowresourcespeech recognition

Klíčová slova v angličtině

Universal acoustic models, topic identification,cross-language information retrieval, transfer learning, lowresourcespeech recognition

Autoři

WIESNER, M.; LIU, C.; ONDEL YANG, L.; HARMAN, C.; MANOHAR, V.; TRMAL, J.; HUANG, Z.; DEHAK, N.; KHUDANPUR, S.

Rok RIV

2020

Vydáno

02.09.2018

Nakladatel

International Speech Communication Association

Místo

Hyderabad

Kniha

Proceedings of Interspeech

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Svazek

2018

Číslo

9

Stát

Francouzská republika

Strany od

2052

Strany do

2056

Strany počet

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"
}

Dokumenty

wiesner_interspeech2018_1836