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1
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In: INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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3
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
In: INTERSPEECH 2021: ; INTERSPEECH 2021: Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03317730 ; INTERSPEECH 2021: Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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4
Automatic risk detection system by audiovisual signal processing ; Système de détection automatique de risques par traitement de signaux audiovisuels
Bendjoudi, Ilyes. - : HAL CCSD, 2021
In: https://tel.archives-ouvertes.fr/tel-03602318 ; Signal and Image processing. Université Polytechnique Hauts-de-France; Institut national des sciences appliquées Hauts-de-France, 2021. English. ⟨NNT : 2021UPHF0040⟩ (2021)
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5
A Comparison of the Affectiva iMotions Facial Expression Analysis Software With EMG for Identifying Facial Expressions of Emotion
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6
Recognizing emotions in spoken dialogue with acoustic and lexical cues
Tian, Leimin. - : The University of Edinburgh, 2018
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7
Emotion and Language: Valence and Arousal Affect Word Recognition
Kuperman, Victor; Estes, Zachary; Brysbaert, Marc. - : American Psychological Association, 2014
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8
Classification of emotional speech using 3DEC hierarchical classifier
In: Speech communication. - Amsterdam [u.a.] : Elsevier 54 (2012) 7, 903-916
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9
Recognising realistic emotions and affect in speech: state of the art and lessons learnt from the first challenge
In: Speech communication. - Amsterdam [u.a.] : Elsevier 53 (2011) 9-10, 1062-1087
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10
Emotion recognition using a hierarchical binary decision tree approach
In: Speech communication. - Amsterdam [u.a.] : Elsevier 53 (2011) 9-10, 1162-1171
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11
Application of speaker- and language identification state-of-the-art techniques for emotion recognition
In: Speech communication. - Amsterdam [u.a.] : Elsevier 53 (2011) 9-10, 1172-1185
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12
The First Facial Expression Recognition and Analysis Challenge
In: ISBN: 978-1-4244-9140-7 ; 2011 IEEE International Conference on Automatic Face & Gesture Recognition (FG 2011) pp. 921-926 (2011)
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13
Automatic emotion recognition: an investigation of acoustic and prosodic parameters
Sethu, Vidhyasaharan, Electrical Engineering & Telecommunications, Faculty of Engineering, UNSW. - : University of New South Wales. Electrical Engineering & Telecommunications, 2009
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14
M.: A Vowel Based Approach For Acted Emotion Recognition
In: http://www.isir.upmc.fr/files/2008COS809.pdf (2008)
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15
Semantic audio-visual data fusion for automatic emotion recognition
In: http://mmi.tudelft.nl/pub/dragos/_datcu_euromedia08.pdf (2008)
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16
Speech Emotion Perception by Human and Machine
In: http://mycite.omikk.bme.hu/doc/52092.pdf (2007)
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17
Emotions, speech and the ASR framework
In: http://www.cs.pitt.edu/~litman/courses/ads/readings/bosch03.pdf (2003)
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