Detail publikačního výsledku

Data selection by sequence summarizing neural network in mismatch condition training

ŽMOLÍKOVÁ, K.; KARAFIÁT, M.; VESELÝ, K.; DELCROIX, M.; WATANABE, S.; BURGET, L.; ČERNOCKÝ, J.

Originální název

Data selection by sequence summarizing neural network in mismatch condition training

Anglický název

Data selection by sequence summarizing neural network in mismatch condition training

Druh

Stať ve sborníku v databázi WoS či Scopus

Originální abstrakt

Data augmentation is a simple and efficient technique to improvethe robustness of a speech recognizer when deployed inmismatched training-test conditions. Our paper proposes a newapproach for selecting data with respect to similarity of acousticconditions. The similarity is computed based on a sequencesummarizing neural network which extracts vectors containingacoustic summary (e.g. noise and reverberation characteristics)of an utterance. Several configurations of this network and differentmethods of selecting data using these "summary-vectors"were explored. The results are reported on a mismatched conditionusing AMI training set with the proposed data selectionand CHiME3 test set.

Anglický abstrakt

Data augmentation is a simple and efficient technique to improvethe robustness of a speech recognizer when deployed inmismatched training-test conditions. Our paper proposes a newapproach for selecting data with respect to similarity of acousticconditions. The similarity is computed based on a sequencesummarizing neural network which extracts vectors containingacoustic summary (e.g. noise and reverberation characteristics)of an utterance. Several configurations of this network and differentmethods of selecting data using these "summary-vectors"were explored. The results are reported on a mismatched conditionusing AMI training set with the proposed data selectionand CHiME3 test set.

Klíčová slova

Automatic speech recognition, Data augmentation,Data selection, Mismatch training condition, Sequencesummarization

Klíčová slova v angličtině

Automatic speech recognition, Data augmentation,Data selection, Mismatch training condition, Sequencesummarization

Autoři

ŽMOLÍKOVÁ, K.; KARAFIÁT, M.; VESELÝ, K.; DELCROIX, M.; WATANABE, S.; BURGET, L.; ČERNOCKÝ, J.

Rok RIV

2017

Vydáno

08.09.2016

Nakladatel

International Speech Communication Association

Místo

San Francisco

ISBN

978-1-5108-3313-5

Kniha

Proceedings of Interspeech 2016

Strany od

2354

Strany do

2358

Strany počet

5

URL

https://www.semanticscholar.org/paper/Data-Selection-by-Sequence-Summarizing-Neural-Zmol%C3%ADkov%C3%A1-Karafi%C3%A1t/bc1832e8b8d4e5edf987e1562b578bd9aa5e18a9

BibTex

@inproceedings{BUT132600,
  author="Kateřina {Žmolíková} and Martin {Karafiát} and Karel {Veselý} and Marc {Delcroix} and Shinji {Watanabe} and Lukáš {Burget} and Jan {Černocký}",
  title="Data selection by sequence summarizing neural network in mismatch condition training",
  booktitle="Proceedings of Interspeech 2016",
  year="2016",
  pages="2354--2358",
  publisher="International Speech Communication Association",
  address="San Francisco",
  doi="10.21437/Interspeech.2016-741",
  isbn="978-1-5108-3313-5",
  url="https://www.semanticscholar.org/paper/Data-Selection-by-Sequence-Summarizing-Neural-Zmol%C3%ADkov%C3%A1-Karafi%C3%A1t/bc1832e8b8d4e5edf987e1562b578bd9aa5e18a9"
}

Dokumenty

zmolikova_interpeech2016_IS160741