Conference paper

MATĚJKA Pavel, PLCHOT Oldřich, SOUFIFAR Mehdi Mohammad, GLEMBEK Ondřej, D'HARO Luis Fernando, VESELÝ Karel, GRÉZL František, MA Jeff, MATSOUKAS Spyros and DEHAK Najim. Patrol Team Language Identification System for DARPA RATS P1 Evaluation. In: Proceedings of Interspeech 2012. Portland, Oregon: International Speech Communication Association, 2012, pp. 1-4. ISBN 978-1-62276-759-5. ISSN 1990-9772. Available from: http://www.isca-speech.org/archive/interspeech_2012/i12_0050.html
Publication language:english
Original title:Patrol Team Language Identification System for DARPA RATS P1 Evaluation
Title (cs):"Patrol Team" Systém pro rozpoznávání jazyka pro evaluaci DARPA RATS P1
Pages:1-4
Proceedings:Proceedings of Interspeech 2012
Conference:Interspeech 2012
Place:Portland, Oregon, US
Year:2012
URL:http://www.isca-speech.org/archive/interspeech_2012/i12_0050.html
ISBN:978-1-62276-759-5
Journal:Proceedings of Interspeech, Vol. 2012, No. 9, FR
ISSN:1990-9772
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2012/matejka_interspeech2012_1295_pp1_4.pdf [PDF]
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2012/matejka_interspeech2012_ICSLPpresentation_Mon_01b_06_nosound.pptx [PPT]
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Keywords
language identification, noisy speech
Annotation
In this paper we present four systems that were part of the Patrol Team Language Identification system for the DARPA RATS project.
Abstract
This paper describes the language identification (LID) system developed by the Patrol team for the first phase of the DARPA RATS (Robust Automatic Transcription of Speech) program, which seeks to advance state of the art detection capabilities on audio from highly degraded communication channels. We show that techniques originally developed for LID on telephone speech (e.g., for the NIST language recognition evaluations) remain effective on the noisy RATS data, provided that careful consideration is applied when designing the training and development sets. In addition, we show significant improvements from the use of Wiener filtering, neural network based and language dependent i-vector modeling, and fusion.
BibTeX:
@INPROCEEDINGS{
   author = {Pavel Mat{\v{e}}jka and Old{\v{r}}ich Plchot and Mohammad
	Mehdi Soufifar and Ond{\v{r}}ej Glembek and Fernando Luis
	D'Haro and Karel Vesel{\'{y}} and Franti{\v{s}}ek
	Gr{\'{e}}zl and Jeff Ma and Spyros Matsoukas and Najim Dehak},
   title = {Patrol Team Language Identification System for DARPA RATS P1
	Evaluation},
   pages = {1--4},
   booktitle = {Proceedings of Interspeech 2012},
   journal = {Proceedings of Interspeech},
   volume = {2012},
   number = {9},
   year = {2012},
   location = {Portland, Oregon, US},
   publisher = {International Speech Communication Association},
   ISBN = {978-1-62276-759-5},
   ISSN = {1990-9772},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=10098}
}

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