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The genetic architecture of the human language connectome ; L'architecture génétique du connectome du langage dans le cerveau humain
Mekki, Yasmina Nozha. - : HAL CCSD, 2022
In: https://tel.archives-ouvertes.fr/tel-03649334 ; Neuroscience. Université Paris-Saclay, 2022. English. ⟨NNT : 2022UPAST019⟩ (2022)
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2
An Overview of Indian Spoken Language Recognition from Machine Learning Perspective
In: ISSN: 2375-4699 ; EISSN: 2375-4702 ; ACM Transactions on Asian and Low-Resource Language Information Processing ; https://hal.inria.fr/hal-03616853 ; ACM Transactions on Asian and Low-Resource Language Information Processing, ACM, In press, ⟨10.1145/3523179⟩ (2022)
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3
Examining the Effect of Acculturation and Language Proficiency on the Psychological Assessment of Spanish-English Bilinguals ...
Woodruff, Miriam. - : Open Science Framework, 2022
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4
Optimal alphabet for single text compression ...
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5
Regression modeling for linguistic data ...
Sonderegger, Morgan. - : Open Science Framework, 2022
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6
Statistics for linguistics with R : a practical introduction
Gries, Stefan Thomas. - Berlin : De Gruyter Mouton, 2021
UB Frankfurt Linguistik
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7
Statistics in corpus linguistics : a new approach
Wallis, Sean. - London : Routledge, 2021
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UB Frankfurt Linguistik
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8
Corpus-based approaches to register variation
Seoane, Elena; Biber, Douglas. - Amsterdam ; Philadelphia : John Benjamins Publishing Company, 2021
Leibniz-Zentrum Allgemeine Sprachwissenschaft
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9
Beke Hansen: Corpus linguistics and sociolinguistics. Leiden: Brill Rodopi, 2018
In: English language and linguistics. - Cambridge : Cambridge Univ. Press 25 (2021) 1, 205-210
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10
Learner corpus research and second language acquisition: an attempt at bridging the gap
In: Learner corpus research meets second language acquisition. - Cambridge, United Kingdom : Cambridge University Press (2021), 1-9
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11
From Error Annotation to Quantitative Analysis: Patterns in Russian Language Learning
In: ISSN: 0036-0252 ; Russian language journal ; https://hal.archives-ouvertes.fr/hal-03376956 ; Russian language journal, American Councils for International Education, Michigan State University 2021, 71 (3), pp.39-70 (2021)
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12
Machine Learning of Motion Statistics Reveals the Kinematic Signature of the Identity of a Person in Sign Language
In: ISSN: 2296-4185 ; Frontiers in Bioengineering and Biotechnology ; https://hal.archives-ouvertes.fr/hal-03298752 ; Frontiers in Bioengineering and Biotechnology, Frontiers, 2021, 9, ⟨10.3389/fbioe.2021.710132⟩ (2021)
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13
A logistic regression model for predicting child language performance ; Un modèle de régression logistique pour la prédiction du développement langagier chez l'enfant
In: SIS 2021, 50th Annuale Conference of the Italian Statistical Society" ; https://hal.archives-ouvertes.fr/hal-03318721 ; SIS 2021, 50th Annuale Conference of the Italian Statistical Society", Jun 2021, Pise, Italy (2021)
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14
The Machine in the Garden of Meter and Rythm
In: Plotting Poetry. On Mechanically-Enhanced Reading ; https://hal.telecom-paris.fr/hal-03255491 ; Bories, Anne-Sophie ; Purnelle, Gérald ; Marchal, Hugues. Plotting Poetry. On Mechanically-Enhanced Reading, Presses universitaires de Liège, 2021, 978-2-87562-280-8 (2021)
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15
Chapter 9. Identification of clusters of lexical areas using geographical factors
In: Language Variation – European Perspectives VIIISelected papers from the Tenth International Conference on Language Variation in Europe (ICLaVE 10), Leeuwarden, June 2019 ; https://halshs.archives-ouvertes.fr/halshs-03272222 ; Hans Van de Velde; Nanna Haug Hilton; Remco Knooihuizen. Language Variation – European Perspectives VIII Selected papers from the Tenth International Conference on Language Variation in Europe (ICLaVE 10), Leeuwarden, June 2019, John Benjamins B.V., pp.210-225, 2021, 978 90 272 5982 0. ⟨10.1075/silv.25.09cha⟩ ; https://www.jbe-platform.com/content/books/9789027259820 (2021)
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16
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
In: ISBN: 978-1-6654-0019-0 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII) ; https://hal.archives-ouvertes.fr/hal-03405970 ; 2021 9th International Conference on Affective Computing and Intelligent Interaction (ACII), Sep 2021, Nara, Japan ; https://www.acii-conf.net/2021/ (2021)
Abstract: International audience ; Recognizing a speaker's emotion from their speech can be a key element in emergency call centers. End-to-end deep learning systems for speech emotion recognition now achieve equivalent or even better results than conventional machine learning approaches. In this paper, in order to validate the performance of our neural network architecture for emotion recognition from speech, we first trained and tested it on the widely used corpus accessible by the community, IEMOCAP. We then used the same architecture as the real life corpus, CEMO, composed of 440 dialogs (2h16m) from 485 speakers. The most frequent emotions expressed by callers in these real life emergency dialogues are fear, anger and positive emotions such as relief. In the IEMOCAP general topic conversations, the most frequent emotions are sadness, anger and happiness. Using the same end-to-end deep learning architecture, an Unweighted Accuracy Recall (UA) of 63% is obtained on IEMOCAP and a UA of 45.6% on CEMO, each with 4 classes. Using only 2 classes (Anger, Neutral), the results for CEMO are 76.9% UA compared to 81.1% UA for IEMOCAP. We expect that these encouraging results with CEMO can be improved by combining the audio channel with the linguistic channel. Real-life emotions are clearly more complex than acted ones, mainly due to the large diversity of emotional expressions of speakers. Index Terms-emotion detection, end-to-end deep learning architecture, call center, real-life database, complex emotions.
Keyword: [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]; [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO.INFO-SD]Computer Science [cs]/Sound [cs.SD]; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; deep learning system; emergency call center; real life; recurrent neural network; speech emotion recognition; temporal neural networks
URL: https://hal.archives-ouvertes.fr/hal-03405970/document
https://hal.archives-ouvertes.fr/hal-03405970/file/main.pdf
https://hal.archives-ouvertes.fr/hal-03405970
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17
The 'nouniness' of attributive adjectives and 'verbiness' of predicative adjectives: evidence from phonology
In: English language and linguistics. - Cambridge : Cambridge Univ. Press 25 (2021) 2, 257-279
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18
Cascading collocations: Collocades as correlates of formulaic language ...
Forsyth, Richard. - : Zenodo, 2021
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19
Cascading collocations: Collocades as correlates of formulaic language ...
Forsyth, Richard. - : Zenodo, 2021
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20
A Codicological and Linguistic Typology of Common Torah Codices from the Cairo Genizah ...
Arrant, Estara. - : Apollo - University of Cambridge Repository, 2021
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