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1
A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments
In: Language Resources and Evaluation Conference (LREC) ; https://hal.archives-ouvertes.fr/hal-01807093 ; Language Resources and Evaluation Conference (LREC), Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Pi, May 2018, Miyazaki, Japan (2018)
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Unsupervised word segmentation from speech with attention
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.
URL: http://eprints.whiterose.ac.uk/150390/
http://eprints.whiterose.ac.uk/150390/7/1308.pdf
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A Very Low Resource Language Speech Corpus for Computational Language Documentation Experiments ...
Godard, P.; Adda, G.; Adda-Decker, M.. - : arXiv, 2017
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