Publication Details

Deep Auto-encoder Based Multi-task Learning Using Probabilistic Transcriptions

DAS Amit, HASEGAWA-JOHNSON Mark and VESELÝ Karel. Deep Auto-encoder Based Multi-task Learning Using Probabilistic Transcriptions. In: Proceedings of Interspeech 2017. Stockholm: International Speech Communication Association, 2017, pp. 2073-2077. ISSN 1990-9772. Available from: http://www.isca-speech.org/archive/Interspeech_2017/pdfs/0582.PDF
Czech title
Multi-task trénování s pravděpodobnostními přepisy založené na hlubokém autoenkodéru
Type
conference paper
Language
english
Authors
Das Amit (UILLINOIS)
Hasegawa-Johnson Mark (UILLINOIS)
Veselý Karel, Ing., Ph.D. (DCGM FIT BUT)
URL
Keywords

cross-lingual speech recognition, probabilistic transcription, deep neural networks, multi-task learning

Abstract

This article is about deep auto-encoder based Multi-task Learning using probabilistic transcriptions.

Published
2017
Pages
2073-2077
Journal
Proceedings of Interspeech, vol. 2017, no. 8, ISSN 1990-9772
Proceedings
Proceedings of Interspeech 2017
Conference
18TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2017), Stockholm, SE
Publisher
International Speech Communication Association
Place
Stockholm, SE
DOI
UT WoS
000457505000434
EID Scopus
BibTeX
@INPROCEEDINGS{FITPUB11585,
   author = "Amit Das and Mark Hasegawa-Johnson and Karel Vesel\'{y}",
   title = "Deep Auto-encoder Based Multi-task Learning Using Probabilistic Transcriptions",
   pages = "2073--2077",
   booktitle = "Proceedings of Interspeech 2017",
   journal = "Proceedings of Interspeech",
   volume = 2017,
   number = 08,
   year = 2017,
   location = "Stockholm, SE",
   publisher = "International Speech Communication Association",
   ISSN = "1990-9772",
   doi = "10.21437/Interspeech.2017-582",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11585"
}
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