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Number of documents

7

William N. Havard — Publications


Computational Linguist


Laurent Besacier   

Conference papers6 documents

  • Marcely Zanon Boito, William N Havard, Mahault Garnerin, Éric Le Ferrand, Laurent Besacier. MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible. Proceedings of The 12th Language Resources and Evaluation Conference, May 2020, Marseille, France. pp.6486 - 6493. ⟨hal-02611059⟩
  • William N Havard, Laurent Besacier, Jean-Pierre Chevrot. Catplayinginthesnow: Impact of Prior Segmentation on a Model of Visually Grounded Speech. Conference on Natural Language Learning (CoNLL), Nov 2020, Virtual, France. ⟨hal-02962275⟩
  • William N Havard, Jean-Pierre Chevrot, Laurent Besacier. Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech. Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Nov 2019, Hong Kong, China. pp.339-348, ⟨10.18653/v1/K19-1032⟩. ⟨hal-02359540⟩
  • William N Havard, Jean-Pierre Chevrot, Laurent Besacier. Models of Visually Grounded Speech Signal Pay Attention to Nouns: A Bilingual Experiment on English and Japanese. International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 2019, Brighton, United Kingdom. pp.8618-8622, ⟨10.1109/ICASSP.2019.8683069⟩. ⟨hal-02013984⟩
  • Xuanli He, Hung Quan, William N Havard, Laurent Besacier, Ingrid Zukerman, et al.. Exploring Textual and Speech information in Dialogue Act Classification with Speaker Domain Adaptation. Australasian Language Technology Association Workshop, Dec 2018, Dunedin, New Zealand. pp.61-65. ⟨hal-01962571⟩
  • William N Havard, Laurent Besacier, Olivier Rosec. SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set. Grounding Language Understanding GLU2017 Workshop (Satellite of Interspeech 2017), Aug 2017, Stockholm, Sweden. ⟨hal-01580879⟩

Poster communications1 document

  • William N Havard, Jean-Pierre Chevrot, Laurent Besacier. Emergence of attention in a neural model of visually grounded speech. Learning Language in Humans and in Machines 2018 conference, Jul 2018, Paris, France. ⟨hal-01970514⟩