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Korean Prosody Phrase Boundary Prediction Model for Speech Synthesis Service in Smart Healthcare
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In: Electronics ; Volume 10 ; Issue 19 (2021)
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Abstract:
Speech processing technology has great potential in the medical field to provide beneficial solutions for both patients and doctors. Speech interfaces, represented by speech synthesis and speech recognition, can be used to transcribe medical documents, control medical devices, correct speech and hearing impairments, and assist the visually impaired. However, it is essential to predict prosody phrase boundaries for accurate natural speech synthesis. This study proposes a method to build a reliable learning corpus to train prosody boundary prediction models based on deep learning. In addition, we offer a way to generate a rule-based model that can predict the prosody boundary from the constructed corpus and use the result to train a deep learning-based model. As a result, we have built a coherent corpus, even though many workers have participated in its development. The estimated pairwise agreement of corpus annotations is between 0.7477 and 0.7916 and kappa coefficient (K) between 0.7057 and 0.7569. In addition, the deep learning-based model based on the rules obtained from the corpus showed a prediction accuracy of 78.57% for the three-level prosody phrase boundary, 87.33% for the two-level prosody phrase boundary.
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Keyword:
bidirectional LSTM-CRF; Korean prosody phrase boundary prediction; rule-based system; speech synthesis; text-to-speech (TTS)
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URL: https://doi.org/10.3390/electronics10192371
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「詞組本位」との関連から見る松下大三郎の漢語「連詞」理論 ; Matsushita Daizaburo's "Conjunctions Theory" of Chinese in relation to Phtase-based Grammar
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TermEval: an automatic metric for evaluating terminology translation in MT
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In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) TermEval: an automatic metric for evaluating terminology translation in MT. In: CICLing 2019, the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 07-13 Apr 2019, La Rochelle, France. (2019)
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TermEval: an automatic metric for evaluating terminology translation in MT
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In: Haque, Rejwanul orcid:0000-0003-1680-0099 , Hasanuzzaman, Mohammed orcid:0000-0003-1838-0091 and Way, Andy orcid:0000-0001-5736-5930 (2019) TermEval: an automatic metric for evaluating terminology translation in MT. In: CICLing 2019: the 20th International Conference on Computational Linguistics and Intelligent Text Processing, 7-13 Apr 2019, La Rochelle, France. (2019)
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APE Shared Task WMT18: Human Post-edits and References Test Data EN-DE PBSMT
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Improved Arabic–Chinese Machine Translation with Linguistic Input Features
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In: Future Internet ; Volume 11 ; Issue 1 (2019)
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Terminology Translation in Low-Resource Scenarios
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In: Information ; Volume 10 ; Issue 9 (2019)
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Fast machine translation on parallel and massively parallel hardware
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Advanced techniques for domain adaptation in Statistical Machine Translation
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10 |
What level of quality can neural machine translation attain on literary text?
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In: Toral, Antonio orcid:0000-0003-2357-2960 and Way, Andy orcid:0000-0001-5736-5930 (2018) What level of quality can neural machine translation attain on literary text? In: Moorkens, Joss orcid:0000-0003-4864-5986 , Castilho, Sheila orcid:0000-0002-8416-6555 , Gaspari, Federico orcid:0000-0003-3808-8418 and Doherty, S, (eds.) Translation Quality Assessment: From Principles to Practice. Machine Translation: Technologies and Applications book series (MATRA), 1 . Springer, Berlin/Heidelberg, 263 -287. ISBN 978-3-319-91240-0 (2018)
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Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian
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In: Articles (2018)
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Is statistical machine translation approach dead?
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In: ICNLSSP 2017 - International Conference on Natural Language, Signal and Speech Processing ; https://hal.inria.fr/hal-01660016 ; ICNLSSP 2017 - International Conference on Natural Language, Signal and Speech Processing, ISGA, Dec 2017, Casablanca, Morocco. pp.1-5 ; http://icnlssp.isga.ma (2017)
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Phrase-Based Language Model in Statistical Machine Translation
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In: ISSN: 0976-0962 ; International Journal of Computational Linguistics and Applications ; https://hal.inria.fr/hal-01336485 ; International Journal of Computational Linguistics and Applications, Alexander Gelbukh, 2016 (2016)
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Exploring linguistic structure for aspect-based sentiment analysis ...
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How much structure is needed: The case of the Persian VP
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In: http://csli-publications.stanford.edu ; Joint 2016 Conference on Head-driven Phrase Structure Grammar and Lexical Functional Grammar ; https://halshs.archives-ouvertes.fr/halshs-01429403 ; Joint 2016 Conference on Head-driven Phrase Structure Grammar and Lexical Functional Grammar, Polish Academy of Sciences, Jul 2016, Varosvie, Poland. pp.236-254 ; http://headlex16.ipipan.waw.pl/ (2016)
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Genuine phrase-based statistical machine translation with supervision
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Integrating Rules and Dictionaries from Shallow-Transfer Machine Translation into Phrase-Based Statistical Machine Translation
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Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced
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Morphology-Aware Alignments for Translation to and from a Synthetic Language
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In: International Workshop on Spoken Language Translation ; https://hal.archives-ouvertes.fr/hal-01635005 ; International Workshop on Spoken Language Translation, Jan 2015, Da Nang, Vietnam (2015)
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