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Automatic Language Identity Tagging on Word and Sentence-Level in Multilingual Text Sources: a Case-Study on Luxembourgish
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In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) ; Ninth International Conference on Language Resources and Evaluation (LREC'14) ; https://hal.archives-ouvertes.fr/hal-01134776 ; Ninth International Conference on Language Resources and Evaluation (LREC'14), European Language Resources Association (ELRA), May 2014, Reykjavik, Iceland. pp.3300-3304 ; http://lrec2014.lrec-conf.org/en/ (2014)
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Speech Alignment and Recognition Experiments for Luxembourgish
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In: Proceedings of the 4th International Workshop on Spoken Language Technologies for Underresourced Languages ; 4th International Workshop on Spoken Language Technologies for Underresourced Languages ; https://hal.archives-ouvertes.fr/hal-01134824 ; 4th International Workshop on Spoken Language Technologies for Underresourced Languages, May 2014, Saint-Petersbourg, Russia. pp.53-60 ; http://www.mica.edu.vn/sltu2014/ (2014)
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Abstract:
International audience ; Luxembourgish, embedded in a multilingual context on the divide between Romance and Germanic cultures, remains one of Europe’s under-described languages. In this paper, we propose to study acoustic similarities between Luxembourgish and major contact languages (German, French, English) with the help of automatic speech alignment and recognition systems. Experiments were run using monolingual acoustic models trained on German, French and English together with (i) “multilingual” models trained on pooled speech data from these three languages, or with (ii) native Luxembourgish acoustic models from 1200 hours of untranscribed Luxembourgish audio data using unsupervised methods. We investigated whether Luxembourgish was globally better represented by one of the individual languages, by the multilingual model or by the native (unsupervised) model. While German provides globally the best acoustic match for native Luxembourgish, detailed analyses reveal language-specific preferences, in particular English and Luxembourgish models are preferred on diphthongs. The first ASR results illustrate the accuracy of the various sets of supervised monolingual and multilingual models versus unsupervised Luxembourgish acoustic models. The ASR word error rate is progressively reduced from 60 to 25% on the development data set by unsupervised training of larger context-dependent models on increasing anounts of audio data.
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Keyword:
[INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; acoustic modeling; forced alignment; language similarity; languages in contact; large vocabulary speech recognition; Luxembourgish; multilingual models; under-resourced languages; unsupervised training
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URL: https://hal.archives-ouvertes.fr/hal-01134824
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A First LVCSR System for Luxembourgish, a Low-Resourced European Language
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In: Human Language Technology Challenges for Computer Science and Linguistics ; https://hal.archives-ouvertes.fr/hal-01135103 ; Zygmunt Vetulani; Joseph Mariani. Human Language Technology Challenges for Computer Science and Linguistics, 8387, Springer International Publishing, pp.479-490, 2014, 5th Language and Technology Conference, LTC 2011, Poznań, Poland, November 25--27, 2011, Revised Selected Papers, 978-3-319-08957-7. ⟨10.1007/978-3-319-08958-4_39⟩ (2014)
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