Automatic language identity tagging on word and sentence-level in multilingual text sources: a case-study on Luxembourgish

• Main Authors: Lavergne, Thomas, Adda, Gilles, Adda-Decker, Martine, Lamel, Lori
• Other Authors: Laboratoire d'Informatique pour la Mécanique et les Sciences de l'Ingénieur (LIMSI), Université Paris-Sud - Paris 11 (UP11)-Sorbonne Université - UFR d'Ingénierie (UFR 919), Sorbonne Université (SU)-Sorbonne Université (SU)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université Paris Saclay (COmUE)
• Format: Conference Object
• Language: English
• Published: HAL CCSD 2014
• Subjects: under-resourced language; language identification; corpus of Luxembourguish; [INFO]Computer Science [cs]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; Iceland
• Online Access: https://hal.archives-ouvertes.fr/hal-01843401

International audience Luxembourgish, embedded in a multilingual context on the divide between Romance and Germanic cultures, remains one of Europe's under-described languages. This is due to the fact that the written production remains relatively low, and linguistic knowledge and resources, such as lexica and pronunciation dictionaries, are sparse. The speakers or writers will frequently switch between Luxembourgish, German, and French, on a per-sentence basis, as well as on a sub-sentence level. In order to build resources like lexicons, and especially pronunciation lexicons, or language models needed for natural language processing tasks such as automatic speech recognition, language used in text corpora should be identified. In this paper, we present the design of a manually annotated corpus of mixed language sentences as well as the tools used to select these sentences. This corpus of difficult sentences was used to test a word-based language identification system. This language identification system was used to select textual data extracted from the web, in order to build a lexicon and language models. This lexicon and language model were used in an Automatic Speech Recognition system for the Luxembourgish language which obtain a 25% WER on the Quaero development data.