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A Large-Scale Study of Machine Translation in the Turkic Languages ...
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A Prototype Free/Open-Source Morphological Analyser and Generator for Sakha ...
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Evaluating Multiway Multilingual NMT in the Turkic Languages ...
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Do RNN States Encode Abstract Phonological Alternations? ...
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A morphological analyser for K’iche’ ; Un analizador morfológico para el idioma k’iche’
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Multi-script morphological transducers and transcribers for seven Turkic languages
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In: Proceedings of the Workshop on Turkic and Languages in Contact with Turkic; Vol 5 (2020); 173-185 ; 2641-3485 (2021)
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SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection ...
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Dependency analysis of noun incorporation in polysynthetic languages ...
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Universal Dependencies v2: An Evergrowing Multilingual Treebank Collection ...
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Common Voice: A Massively-Multilingual Speech Corpus ...
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Ardila, Rosana; Branson, Megan; Davis, Kelly; Henretty, Michael; Kohler, Michael; Meyer, Josh; Morais, Reuben; Saunders, Lindsay; Tyers, Francis M.; Weber, Gregor. - : arXiv, 2019
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Abstract:
The Common Voice corpus is a massively-multilingual collection of transcribed speech intended for speech technology research and development. Common Voice is designed for Automatic Speech Recognition purposes but can be useful in other domains (e.g. language identification). To achieve scale and sustainability, the Common Voice project employs crowdsourcing for both data collection and data validation. The most recent release includes 29 languages, and as of November 2019 there are a total of 38 languages collecting data. Over 50,000 individuals have participated so far, resulting in 2,500 hours of collected audio. To our knowledge this is the largest audio corpus in the public domain for speech recognition, both in terms of number of hours and number of languages. As an example use case for Common Voice, we present speech recognition experiments using Mozilla's DeepSpeech Speech-to-Text toolkit. By applying transfer learning from a source English model, we find an average Character Error Rate improvement of ... : Accepted to LREC 2020 ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG
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URL: https://dx.doi.org/10.48550/arxiv.1912.06670 https://arxiv.org/abs/1912.06670
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