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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 4 5...113 Hits 1 – 20 of 2.250 1 The contextual logic Kohler, Arnaud In: https://hal.archives-ouvertes.fr/hal-03195162 ; 2022 (2022) BASE Show details 2 Learning and controlling the source-filter representation of speech with a variational autoencoder Sadok, Samir; Leglaive, Simon; Girin, Laurent... In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022) BASE Show details 3 Assessing the impact of OCR noise on multilingual event detection over digitised documents Boros, Emanuela; Nguyen, Nhu Khoa; Lejeune, Gaël... In: ISSN: 1432-5012 ; EISSN: 1432-1300 ; International Journal on Digital Libraries ; https://hal.archives-ouvertes.fr/hal-03635985 ; International Journal on Digital Libraries, Springer Verlag, 2022, ⟨10.1007/s00799-022-00325-2⟩ (2022) BASE Show details 4 Introducing the HIPE 2022 Shared Task: Named Entity Recognition and Linking in Multilingual Historical Documents Ehrmann, Maud; Romanello, Matteo; Doucet, Antoine... In: Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II ; https://hal.archives-ouvertes.fr/hal-03635971 ; Matthias Hagen; Suzan Verberne; Craig Macdonald; Christin Seifert; Krisztian Balog; Kjetil Nørvåg; Vinay Setty. Advances in Information Retrieval. 44th European Conference on IR Research, ECIR 2022, Stavanger, Norway, April 10–14, 2022, Proceedings, Part II, 13186, Springer International Publishing, pp.347-354, 2022, Lecture Notes in Computer Science, 978-3-030-99738-0. ⟨10.1007/978-3-030-99739-7_44⟩ (2022) BASE Show details 5 Can Character-based Language Models Improve Downstream Task Performance in Low-Resource and Noisy Language Scenarios? Riabi, Arij; Sagot, Benoît; Seddah, Djamé In: Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021) ; https://hal.inria.fr/hal-03527328 ; Seventh Workshop on Noisy User-generated Text (W-NUT 2021, colocated with EMNLP 2021), Jan 2022, punta cana, Dominican Republic ; https://aclanthology.org/2021.wnut-1.47/ (2022) BASE Show details 6 Annotation of Morphological Errors in L2 Russian Corpus Analysis Kor Chahine, Irina; Uetova, Ekaterina In: 21st Annual Second Language Acquisition and Teaching Interdisciplinary Roundtable ; https://hal.archives-ouvertes.fr/hal-03620469 ; 21st Annual Second Language Acquisition and Teaching Interdisciplinary Roundtable, University of Arizona, Feb 2022, Tucson, United States (2022) BASE Show details 7 Cross-Situational Learning Towards Robot Grounding OOTA, Subba Reddy; Alexandre, Frédéric; Hinaut, Xavier In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022) BASE Show details 8 Cross-Situational Learning Towards Robot Grounding OOTA, Subba Reddy; Alexandre, Frédéric; Hinaut, Xavier In: https://hal.archives-ouvertes.fr/hal-03628290 ; 2022 (2022) BASE Show details 9 A Methodology for the Comparison of Human Judgments With Metrics for Coreference Resolution Borovikova, Mariya; Grobol, Loïc; Lefeuvre-Halftermeyer, Anaïs... In: HumEval at ACL ; https://hal.archives-ouvertes.fr/hal-03650294 ; HumEval at ACL, May 2022, Dublin, Ireland ; https://humeval.github.io/ (2022) BASE Show details 10 Can machines learn to see without visual databases? Betti, Alessandro; Gori, Marco; Melacci, Stefano... In: https://hal.archives-ouvertes.fr/hal-03526569 ; 2022 (2022) BASE Show details 11 A comparative study of several parameterizations for speaker recognition ... Faundez-Zanuy, Marcos. - : arXiv, 2022 BASE Show details 12 A Neural Pairwise Ranking Model for Readability Assessment ... Lee, Justin; Vajjala, Sowmya. - : arXiv, 2022 BASE Show details 13 Compiler-Driven Simulation of Reconfigurable Hardware Accelerators ... Li, Zhijing; Ye, Yuwei; Neuendorffer, Stephen. - : arXiv, 2022 BASE Show details 14 Subspace-based Representation and Learning for Phonotactic Spoken Language Recognition ... Lee, Hung-Shin; Tsao, Yu; Jeng, Shyh-Kang. - : arXiv, 2022 BASE Show details 15 ClassSPLOM -- A Scatterplot Matrix to Visualize Separation of Multiclass Multidimensional Data ... Aupetit, Michael; Ali, Ahmed. - : arXiv, 2022 BASE Show details 16 A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets ... Mhamed, Mustafa; Sutcliffe, Richard; Sun, Xia. - : arXiv, 2022 BASE Show details 17 Mono vs Multilingual BERT: A Case Study in Hindi and Marathi Named Entity Recognition ... Litake, Onkar; Sabane, Maithili; Patil, Parth. - : arXiv, 2022 BASE Show details 18 Informative Causality Extraction from Medical Literature via Dependency-tree based Patterns ... Kabir, Md. Ahsanul; Almulhim, AlJohara; Luo, Xiao. - : arXiv, 2022 BASE Show details 19 Learning and controlling the source-filter representation of speech with a variational autoencoder ... Sadok, Samir; Leglaive, Simon; Girin, Laurent. - : arXiv, 2022 BASE Show details 20 Correcting Misproducted Speech using Spectrogram Inpainting ... Ben-Simon, Talia; Kreuk, Felix; Awwad, Faten; Cohen, Jacob T.; Keshet, Joseph. - : arXiv, 2022 Abstract: Learning a new language involves constantly comparing speech productions with reference productions from the environment. Early in speech acquisition, children make articulatory adjustments to match their caregivers' speech. Grownup learners of a language tweak their speech to match the tutor reference. This paper proposes a method to synthetically generate correct pronunciation feedback given incorrect production. Furthermore, our aim is to generate the corrected production while maintaining the speaker's original voice. The system prompts the user to pronounce a phrase. The speech is recorded, and the samples associated with the inaccurate phoneme are masked with zeros. This waveform serves as an input to a speech generator, implemented as a deep learning inpainting system with a U-net architecture, and trained to output a reconstructed speech. The training set is composed of unimpaired proper speech examples, and the generator is trained to reconstruct the original proper speech. We evaluated the ... : under submission to Interspeech 2022 ... Keyword: Audio and Speech Processing eess.AS; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Machine Learning cs.LG URL: https://dx.doi.org/10.48550/arxiv.2204.03379 https://arxiv.org/abs/2204.03379 BASE Hide details Page: 1 2 3 4 5...113

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