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Face Biometric Spoof Detection Method Using a Remote Photoplethysmography Signal
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In: Sensors; Volume 22; Issue 8; Pages: 3070 (2022)
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A Portable Sign Language Collection and Translation Platform with Smart Watches Using a BLSTM-Based Multi-Feature Framework
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In: Micromachines; Volume 13; Issue 2; Pages: 333 (2022)
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American Sign Language Words Recognition of Skeletal Videos Using Processed Video Driven Multi-Stacked Deep LSTM
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In: Sensors; Volume 22; Issue 4; Pages: 1406 (2022)
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Phonemic interference in short-term memory contributes to forgetting but is not due to overwriting
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In: Test Series for Scopus Harvesting 2021 (2022)
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Deep Learning Methods for Human Behavior Recognition
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Lu, Jia. - : Auckland University of Technology, 2021
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| 6 |
Auditory and visual short-term memory: Influence of material type, contour, and musical expertise
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In: ISSN: 0340-0727 ; EISSN: 1430-2772 ; Psychological Research ; https://hal.archives-ouvertes.fr/hal-03384372 ; Psychological Research, Springer Verlag, In press, ⟨10.1007/s00426-021-01519-0⟩ (2021)
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Learning emotions latent representation with CVAE for Text-Driven Expressive AudioVisual Speech Synthesis
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In: ISSN: 0893-6080 ; Neural Networks ; https://hal.inria.fr/hal-03204193 ; Neural Networks, Elsevier, 2021, 141, pp.315-329. ⟨10.1016/j.neunet.2021.04.021⟩ (2021)
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Abstract:
International audience ; Great improvement has been made in the field of expressive audiovisual Text-to-Speech synthesis (EAVTTS) thanks to deep learning techniques. However, generating realistic speech is still an open issue and researchers in this area have been focusing lately on controlling the speech variability.In this paper, we use different neural architectures to synthesize emotional speech. We study the application of unsupervised learning techniques for emotional speech modeling as well as methods for restructuring emotions representation to make it continuous and more flexible. This manipulation of the emotional representation should allow us to generate new styles of speech by mixing emotions. We first present our expressive audiovisual corpus. We validate the emotional content of this corpus with three perceptual experiments using acoustic only, visual only and audiovisual stimuli.After that, we analyze the performance of a fully connected neural network in learning characteristics specific to different emotions for the phone duration aspect and the acoustic and visual modalities.We also study the contribution of a joint and separate training of the acoustic and visual modalities in the quality of the generated synthetic speech.In the second part of this paper, we use a conditional variational auto-encoder (CVAE) architecture to learn a latent representation of emotions. We applied this method in an unsupervised manner to generate features of expressive speech. We used a probabilistic metric to compute the overlapping degree between emotions latent clusters to choose the best parameters for the CVAE. By manipulating the latent vectors, we were able to generate nuances of a given emotion and to generate new emotions that do not exist in our database. For these new emotions, we obtain a coherent articulation. We conducted four perceptual experiments to evaluate our findings.
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Keyword:
[MATH.MATH-MG]Mathematics [math]/Metric Geometry [math.MG]; [SCCO.COMP]Cognitive science/Computer science; [SCCO.LING]Cognitive science/Linguistics; [SDV.OT]Life Sciences [q-bio]/Other [q-bio.OT]; [SHS.INFO]Humanities and Social Sciences/Library and information sciences; [SHS.LANGUE]Humanities and Social Sciences/Linguistics; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing; [STAT.ML]Statistics [stat]/Machine Learning [stat.ML]; bidirectional long short-term memory (BLSTM); conditional variationalauto-encoder; deeplearning; emotion; Expressive audiovisual speech synthesis; Expressive talking avatar; facial expression
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URL: https://doi.org/10.1016/j.neunet.2021.04.021
https://hal.inria.fr/hal-03204193/document
https://hal.inria.fr/hal-03204193
https://hal.inria.fr/hal-03204193/file/neural_networks_journal-8.pdf
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Research compendium for Montero-Melis et al. (2021) "No evidence for embodiment: The motor system is not needed to keep action words in working memory" (Cortex) ...
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Cross-cultural cognitive assessment of dementia: a meta-analysis of the impact of illiteracy on dementia screening and an evaluation of a transcultural short-term memory assessment ...
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| 11 |
Human Gait Phase Recognition in Embedded Sensor System
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Liu, Zhenbang. - : KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021
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| 12 |
Forecasting Hotel Room Occupancy Using Long Short-Term Memory Networks with Sentiment Analysis and Scores of Customer Online Reviews
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In: Applied Sciences ; Volume 11 ; Issue 21 (2021)
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| 13 |
Dynamic gesture classification of American Sign Language using deep learning
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| 14 |
Asm2Seq: Explainable Assembly Code Functional Summary Generation
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| 15 |
Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting
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In: Graduate Theses and Dissertations (2021)
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| 16 |
Cross-cultural cognitive assessment of dementia: a meta-analysis of the impact of illiteracy on dementia screening and an evaluation of a transcultural short-term memory assessment
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The Effect of Language Recognition in Music on Short-Term Memory Recall and Physiological Stress Response
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| 18 |
The relationship between cognitive ability and BOLD activation across sleep–wake states
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In: Brain and Mind Institute Researchers' Publications (2021)
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Generating Effective Sentence Representations: Deep Learning and Reinforcement Learning Approaches
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In: Electronic Thesis and Dissertation Repository (2021)
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Tipo de erro e nível socioeconômico em tarefa de repetição de não palavras ; Type of error and socioeconomic status in non-word repetition task
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