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Hits 1 – 20 of 110
1
Artificial Intelligence in Digestive Endoscopy—Where Are We and Where Are We Going?
Radu-Alexandru Vulpoi; Mihaela Luca; Adrian Ciobanu; Andrei Olteanu; Oana-Bogdana Barboi; Vasile Liviu Drug
In: Diagnostics; Volume 12; Issue 4; Pages: 927 (2022)
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2
Online And Face To Face Teaching: two complementary educational intervention modes ; L'enseignement du FLE en face à face et en ligne : deux modes d'intervention pédagogiques complémentaires
Narainen, Stelene
In: ISSN: 2773-286X ; Didaskein ; https://hal.archives-ouvertes.fr/hal-03429054 ; Didaskein, 2021, 2 (1), pp.28-47 (2021)
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3
Reading Robot
Watts, Gillian
;
Myers, Andrew
;
Tan, Sabrinna
...
In: General Engineering (2020)
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4
Improving Mispronunciation Detection of Arabic Words for Non-Native Learners Using Deep Convolutional Neural Network Features
Shamila Akhtar
;
Fawad Hussain
;
Fawad Riasat Raja
;
Muhammad Ehatisham-ul-haq
;
Naveed Khan Baloch
;
Farruh Ishmanov
;
Yousaf Bin Zikria
In: Electronics ; Volume 9 ; Issue 6 (2020)
Abstract:
Computer-Aided Language Learning (CALL) is growing nowadays because learning new languages is essential for communication with people of different linguistic backgrounds. Mispronunciation detection is an integral part of CALL, which is used for automatic pointing of errors for the non-native speaker. In this paper, we investigated the mispronunciation detection of Arabic words using deep Convolution Neural Network (CNN). For automated pronunciation error detection, we proposed CNN features-based model and extracted features from different layers of Alex Net (layers 6, 7, and 8) to train three machine learning classifiers ; K-nearest neighbor (KNN), Support Vector Machine (SVM) and Random Forest (RF). We also used a transfer learning-based model in which feature extraction and classification are performed automatically. To evaluate the performance of the proposed method, a comprehensive evaluation is provided on these methods with a traditional machine learning-based method using Mel Frequency Cepstral Coefficients (MFCC) features. We used the same three classifiers KNN, SVM, and RF in the baseline method for mispronunciation detection. Experimental results show that with handcrafted features, transfer learning-based method and classification based on deep features extracted from Alex Net achieved an average accuracy of 73.67, 85 and 93.20 on Arabic words, respectively. Moreover, these results reveal that the proposed method with feature selection achieved the best average accuracy of 93.20% than all other methods.
Keyword:
computer-aided language learning
;
deep convolutional neural network
;
mel frequency cepstral coefficients (MFCC)
;
mispronunciation detection
;
transfer learning
URL:
https://doi.org/10.3390/electronics9060963
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5
Designing, translation: learning and evaluation of a Greek/English writing to Braille
Aggelis, Alexandros
In: Journal of Contemporary Education, Theory & Research ; 2 ; 16-21 (2020)
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6
Artificial Intelligence and its future potential in lung cancer screening
Joy Mathew, Christopher
;
David, Ashwini Maria
;
Joy Mathew, Chris Mariya
. - : IfADo - Leibniz Research Centre for Working Environment and Human Factors, Dortmund, 2020
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7
Project-based learning: A practical approach to implementing Memsource in the classroom
Herget, Katrin
. - : Editorial Universitat Politècnica de València, 2020
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8
CALLIDUS -- Computer-Aided Language Learning: Lexikonerwerb im Lateinunterricht durch korpusgestützte Methoden ...
Schulz, Konstantin
. - : Zenodo, 2019
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9
CALLIDUS -- Computer-Aided Language Learning: Lexikonerwerb im Lateinunterricht durch korpusgestützte Methoden ...
Schulz, Konstantin
. - : Zenodo, 2019
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10
CALLIDUS: Intelligent Software Infrastructure for Teaching Latin ...
Schulz, Konstantin
. - : Zenodo, 2019
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11
CALLIDUS: Intelligent Software Infrastructure for Teaching Latin ...
Schulz, Konstantin
. - : Zenodo, 2019
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12
A vision of miking : Interactive programmatic modeling, sound language composition, and self-learning compilation
Broman, David
. - : KTH, Programvaruteknik och datorsystem, SCS, 2019. : Association for Computing Machinery, Inc, 2019
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13
Дистанционный курс письменной коммуникации по немецкому языку как иностранному
Mueller, Julia Eduardovna
In: Nauchnyy Dialog / Scientific Dialogue ; 10 ; 324-333 (2019)
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14
Принципы организации смешанного курса по немецкому языку как иностранному на платформе Moodle
Mueller, Julia Eduardovna
In: Nauchnyy Dialog / Scientific Dialogue ; 4 ; 340-352 (2019)
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15
Modeling language learning using specialized Elo ratings
J. Hou
;
K. Maximilian
;
J.M. Hoya Quecedo
. - : The Association for Computational Linguistics, 2019. : place:Stroudsburg, 2019
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16
CALLIDUS: Natural Language Processing and Empirical Studies for Teaching Latin ...
Beyer, Andrea
;
Schulz, Konstantin
. - : Zenodo, 2018
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17
CALLIDUS: Natural Language Processing and Empirical Studies for Teaching Latin ...
Beyer, Andrea
;
Schulz, Konstantin
. - : Zenodo, 2018
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18
COST ELN IS1401 Writing Support Tools Review Database
Devitt, Ann
. - : European Commission COST, 2018
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19
Phonological Feature Based Mispronunciation Detection and Diagnosis using Multi-Task DNNs and Active Learning
Arora, V
;
Lahiri, A
;
Reetz, H
. - 2017
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20
An investigation of changes in L2 writing anxiety between blended and conventional English language learning context
Bailey, Daniel R.
;
Lee, Andrea Rakushin
;
Vorst, Tommy Che
. - : Asia-Pacific Association for Computer-Assisted Language Learning, 2017
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