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Parallel Corpora in Mboshi (Bantu C25, Congo-Brazzaville)
In: 11th edition of the Language Resources and Evaluation Conference (LREC 2018) ; https://hal.archives-ouvertes.fr/hal-01710043 ; 11th edition of the Language Resources and Evaluation Conference (LREC 2018), ELRA, May 2018, Miyazaki, Japan (2018)
Abstract: International audience ; This article presents multimodal and parallel data collections in Mboshi, as part of the French-German BULB project. It aims at supporting documentation and providing digital resources for less resourced languages with the help of speech and language-based technology. The data collection specifications thus have to meet both field linguists' and computer scientists' requirements, which are large corpora for the latter and linguistically dense data for the former. Beyond speech, the collection comprises pictures and videos documenting social practices, agriculture, wildlife and plants. Visual supports aimed at encouraging people to comment on objects which are meaningful in their daily lives. Speech recordings are composed of the original speech in Mboshi, a respoken version and a translated version to French. These three speech streams remain time-aligned thanks to LIG-AIKUMA, which adds new features to a previous AIKUMA application. The speech corpus includes read material (5k sentences, Bible), verb conjugations and a large part of spontaneous speech (conversations, picture descriptions) resulting in over 50 hours of Mboshi speech, of which 20 hours are already respoken and orally translated to French. These parallel oral data are intended for linguistic documentation (tonology, phonology.) and automatic processing (corpus annotation, alignment between Mboshi speech and French translations).
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; ASR; Bantu; fieldwork; French; images; Mboshi; parallel corpus; speech
URL: https://hal.archives-ouvertes.fr/hal-01710043/document
https://hal.archives-ouvertes.fr/hal-01710043
https://hal.archives-ouvertes.fr/hal-01710043/file/LREC%202018_Rialland.pdf
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