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arXiv · 1806.06734

Unsupervised Word Segmentation from Speech with Attention

Pierre Godard·Marcely Zanon-Boito·Lucas Ondel·Alexandre Berard·François Yvon·Aline Villavicencio·Laurent Besacier
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Abstract

We present a first attempt to perform attentional word segmentation directly from the speech signal, with the final goal to automatically identify lexical units in a low-resource, unwritten language (UL). Our methodology assumes a pairing between recordings in the UL with translations in a well-resourced language. It uses Acoustic Unit Discovery (AUD) to convert speech into a sequence of pseudo-phones that is segmented using neural soft-alignments produced by a neural machine translation model. Evaluation uses an actual Bantu UL, Mboshi; comparisons to monolingual and bilingual baselines illustrate the potential of attentional word segmentation for language documentation.

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Pierre Godard, Marcely Zanon-Boito, Lucas Ondel, Alexandre Berard, François Yvon, Aline Villavicencio, Laurent Besacier. 2018-06-18. Unsupervised Word Segmentation from Speech with Attention. https://arxiv.org/abs/1806.06734

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