Word Learning Prosodic Information by Berndt Daniel (4 results)

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  • Language: English

    Published by VDM Verlag Dr. Müller, 2010

    363931414X / 9783639314144

    • Softcover

    Seller: preigu, Osnabrück, Germanypreigu

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    Taschenbuch. Condition: Neu. Word Learning with Prosodic information | Applying non-verbal Cues to enhance Speech Recognition Performance | Daniel Berndt | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639314144 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

  • Language: English

    Published by VDM Verlag Dr. Müller, 2010

    363931414X / 9783639314144

    • Softcover

    Seller: Mispah books, Redhill, SURRE, United KingdomMispah books

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    paperback. Condition: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

  • Language: English

    Published by VDM Verlag Dr. Müller, 2010

    363931414X / 9783639314144

    • Softcover
    • Print on Demand

    Seller: moluna, Greven, Germanymoluna

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    Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Berndt DanielBorn 1988 in Rostock, Germany, Daniel Berndt studied Cognitive Science in Osnabrueck and Artificial Intelligence in Edinburgh. He became co-founder of Codeus, a developing company applying techniques from AI and Computa.

  • Language: English

    Published by VDM Verlag Dr. Müller, 2010

    363931414X / 9783639314144

    • Softcover
    • Print on Demand

    Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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    Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Previous studies have shown that infants use prosodic information as an aid for discriminating and recognising words. The present work is based on a word learning model which automatically extracts target words from raw speech input paired with a label for the target word. This model was enhanced by incorporating prosodic information. In addition, an unsupervised model is developed which does not rely on a label of any kind. Although prosodic information could not improve the performance of the unsupervised model, it is shown that the incorporation of pitch leads to an increase of performance in the supervised case and that the unsupervised model yields effective results.