Découverte non supervisée de lexique à partir d'un corpus multimodal pour la documentation des langues en danger

Abstract : Many languages are on the brink of extinction and many disappear each and every year at a rate never seen before. Field linguists lack the time and the means to document and describe all of them before they die out. The goal of our work is to help them in their task, make it easier and speed up the data processing and annotation tasks. In this dissertation, we propose methods to use an unsupervised term discovery (UTD) system to extract lexicon from multimodal corpora consisting of speech and images. We also propose a method using information retrieval techniques to hypothesise the meaning of the discovered lexical items. In the first place, this dissertation presents the creation of a large multimodal corpus which includes speech and images. This corpus simulating that of an endangered language will allow us evaluate the performances of an unsupervised term discovery system. In the second place, we apply an unsupervised term discovery system based on segmental dynamic time warping (S-DTW) to a large synthetic multimodal corpus and also to the multimodal corpus of a real endangered language called Mboshi, spoken in Congo-Brazzaville.
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Master Thesis
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https://dumas.ccsd.cnrs.fr/dumas-01562024
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Submitted on : Thursday, July 13, 2017 - 2:58:44 PM
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  • HAL Id : dumas-01562024, version 1

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William Havard. Découverte non supervisée de lexique à partir d'un corpus multimodal pour la documentation des langues en danger. Sciences de l'Homme et Société. 2017. ⟨dumas-01562024⟩

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