Conference paper

ŽMOLÍKOVÁ Kateřina, KARAFIÁT Martin, VESELÝ Karel, DELCROIX Marc, WATANABE Shinji, BURGET Lukáš and ČERNOCKÝ Jan. Data selection by sequence summarizing neural network in mismatch condition training. In: Proceedings of Interspeech 2016. San Francisco: International Speech Communication Association, 2016, pp. 2354-2358. ISBN 978-1-5108-3313-5. Available from: https://www.semanticscholar.org/paper/Data-Selection-by-Sequence-Summarizing-Neural-Zmol%C3%ADkov%C3%A1-Karafi%C3%A1t/bc1832e8b8d4e5edf987e1562b578bd9aa5e18a9
Publication language:english
Original title:Data selection by sequence summarizing neural network in mismatch condition training
Title (cs):Výběr dat pomocí sekvenční sumarizační neuronové sítě v trénování na datech z odlišných podmínek
Pages:2354-2358
Proceedings:Proceedings of Interspeech 2016
Conference:Interspeech 2016
Place:San Francisco, US
Year:2016
URL:https://www.semanticscholar.org/paper/Data-Selection-by-Sequence-Summarizing-Neural-Zmol%C3%ADkov%C3%A1-Karafi%C3%A1t/bc1832e8b8d4e5edf987e1562b578bd9aa5e18a9
ISBN:978-1-5108-3313-5
Publisher:International Speech Communication Association
URL:http://www.fit.vutbr.cz/research/groups/speech/publi/2016/zmolikova_interpeech2016_IS160741.pdf [PDF]
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Keywords
Automatic speech recognition, Data augmentation, Data selection, Mismatch training condition, Sequence summarization
Annotation
Data augmentation is a simple and efficient technique to improve the robustness of a speech recognizer when deployed in mismatched training-test conditions. Our paper proposes a new approach for selecting data with respect to similarity of acoustic conditions. The similarity is computed based on a sequence summarizing neural network which extracts vectors containing acoustic summary (e.g. noise and reverberation characteristics) of an utterance. Several configurations of this network and different methods of selecting data using these "summary-vectors" were explored. The results are reported on a mismatched condition using AMI training set with the proposed data selection and CHiME3 test set.
Abstract
Data augmentation is a simple and efficient technique to improve the robustness of a speech recognizer when deployed in mismatched training-test conditions. Our paper proposes a new approach for selecting data with respect to similarity of acoustic conditions. The similarity is computed based on a sequence summarizing neural network which extracts vectors containing acoustic summary (e.g. noise and reverberation characteristics) of an utterance. Several configurations of this network and different methods of selecting data using these "summary-vectors" were explored. The results are reported on a mismatched condition using AMI training set with the proposed data selection and CHiME3 test set.
BibTeX:
@INPROCEEDINGS{
   author = {Kate{\v{r}}ina {\v{Z}}mol{\'{i}}kov{\'{a}} and Martin
	Karafi{\'{a}}t and Karel Vesel{\'{y}} and Marc Delcroix and
	Shinji Watanabe and Luk{\'{a}}{\v{s}} Burget and Jan
	{\v{C}}ernock{\'{y}}},
   title = {Data selection by sequence summarizing neural network in
	mismatch condition training},
   pages = {2354--2358},
   booktitle = {Proceedings of Interspeech 2016},
   year = {2016},
   location = {San Francisco, US},
   publisher = {International Speech Communication Association},
   ISBN = {978-1-5108-3313-5},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php?id=11271}
}

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