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Emotional Speech Recognition Using Deep Neural Networks
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In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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Evaluation of Speaker Anonymization on Emotional Speech ; Analyse de l'anonymisation du locuteur sur de la parole émotionnelle
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In: JEP2022 - Journées d'Études sur la Parole ; https://hal.archives-ouvertes.fr/hal-03636737 ; JEP2022 - Journées d'Études sur la Parole, Jun 2022, Île de Noirmoutier, France (2022)
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Contextual time-continuous emotion recognition based on multimodal data ...
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| 4 |
Emotional Speech Recognition Method Based on Word Transcription
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In: Sensors; Volume 22; Issue 5; Pages: 1937 (2022)
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| 5 |
Emotional Speech Recognition Using Deep Neural Networks
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In: Sensors; Volume 22; Issue 4; Pages: 1414 (2022)
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Advanced Fusion-Based Speech Emotion Recognition System Using a Dual-Attention Mechanism with Conv-Caps and Bi-GRU Features
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In: Electronics; Volume 11; Issue 9; Pages: 1328 (2022)
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The Emotion Probe: On the Universality of Cross-Linguistic and Cross-Gender Speech Emotion Recognition via Machine Learning
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In: Sensors; Volume 22; Issue 7; Pages: 2461 (2022)
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Research on Speech Emotion Recognition Based on AA-CBGRU Network
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In: Electronics; Volume 11; Issue 9; Pages: 1409 (2022)
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Abstract:
Speech emotion recognition is an emerging research field in the 21st century, which is of great significance to human–computer interaction. In order to enable various smart devices to better recognize and understand the emotions contained in human speech, in view of the problems of gradient disappearance and poor learning ability of the time series information in the current speech emotion classification model, an AA-CBGRU network model is proposed for speech emotion recognition. The model first extracts the spectrogram and its first and second order derivative features of the speech signal, then extracts the spatial features of the inputs through the convolutional neural network with residual blocks, then uses the BGRU network with an attention layer to mine deep time series information, and finally uses the full connection layer to achieve the final emotion recognition. The experimental results on the IEMOCAP sentiment corpus show that the model in this paper improves both the weighted accuracy (WA) and the unweighted accuracy (UA).
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Keyword:
attention mechanism; bidirectional gated recurrent unit; human–computer interaction; residual block; speech emotion recognition
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URL: https://doi.org/10.3390/electronics11091409
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| 9 |
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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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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| 10 |
Speaker Attentive Speech Emotion Recognition
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In: Proccedings of interspeech 2021 ; Interspeech 2021 ; https://hal.archives-ouvertes.fr/hal-03554368 ; Interspeech 2021, Aug 2021, Brno, Czech Republic. pp.2866-2870, ⟨10.21437/interspeech.2021-573⟩ (2021)
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| 11 |
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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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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| 12 |
LeBenchmark: A Reproducible Framework for Assessing Self-Supervised Representation Learning from Speech
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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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Automatic risk detection system by audiovisual signal processing ; Système de détection automatique de risques par traitement de signaux audiovisuels
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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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On the use of Self-supervised Pre-trained Acoustic and Linguistic Features for Continuous Speech Emotion Recognition
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In: IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03003469 ; IEEE Spoken Language Technology Workshop, Jan 2021, Virtual, China (2021)
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| 15 |
Evaluation of Speaker Anonymization on Emotional Speech
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In: 1st ISCA Symposium on Security and Privacy in Speech Communication ; https://hal.inria.fr/hal-03377797 ; 1st ISCA Symposium on Security and Privacy in Speech Communication, Nov 2021, Virtual, Germany (2021)
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| 16 |
End-to-End Speech Emotion Recognition: Challenges of Real-Life Emergency Call Centers Data Recordings
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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)
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"You made me feel this way": Investigating Partners' Influence in Predicting Emotions in Couples' Conflict Interactions using Speech Data ...
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Surviving in a second language: survival processing effect in memory of bilinguals
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| 19 |
Multi-Modal Emotion Recognition Using Speech Features and Text-Embedding
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In: Applied Sciences ; Volume 11 ; Issue 17 (2021)
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| 20 |
Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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