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Representations of language in a model of visually grounded speech signal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

We present a visually grounded model of speech perception which projects spoken utterances and images to a joint semantic space. We use a multi-layer recurrent highway network to model the temporal nature of spoken speech, and show that it learns to extract both form and meaning-based linguistic knowledge from the input signal. We carry out an in-depth analysis of the representations used by different components of the trained model and show that encoding of semantic aspects tends to become richer as we go up the hierarchy of layers, whereas encoding of form-related aspects of the language input tends to initially increase and then plateau or decrease.
Original languageEnglish
Title of host publicationProceedings of the 55th of the Annual Meeting of the Association for Computational Linguistics
PublisherAssociation for Computational Linguistics
StatePublished - 2017
EventAnnual Meeting of the Association for Computational Linguistics - Vancouver, Canada
Duration: 30 Jul 20174 Aug 2017
Conference number: 55


ConferenceAnnual Meeting of the Association for Computational Linguistics
Abbreviated titleACL 2017
Internet address



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