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When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification
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In: BMC Med Inform Decis Mak (2022)
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Kinematic Motion Retargeting via Neural Latent Optimization for Learning Sign Language ...
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CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation ...
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Special Patterns of Dynamic Brain Networks Discriminate Between Face and Non-face Processing: A Single-Trial EEG Study
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In: Front Neurosci (2021)
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Dynamic Movement Primitive based Motion Retargeting for Dual-Arm Sign Language Motions ...
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ADFAC: Automatic detection of facial articulatory features
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In: MethodsX (2020)
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Abstract:
Using computer-vision and image processing techniques, we aim to identify specific visual cues as induced by facial movements made during monosyllabic speech production. The method is named ADFAC: Automatic Detection of Facial Articulatory Cues. Four facial points of interest were detected automatically to represent head, eyebrow and lip movements: nose tip (proxy for head movement), medial point of left eyebrow, and midpoints of the upper and lower lips. The detected points were then automatically tracked in the subsequent video frames. Critical features such as the distance, velocity, and acceleration describing local facial movements with respect to the resting face of each speaker were extracted from the positional profiles of each tracked point. In this work, a variant of random forest is proposed to determine which facial features are significant in classifying speech sound categories. The method takes in both video and audio as input and extracts features from any video with a plain or simple background. The method is implemented in MATLAB and scripts are made available on GitHub for easy access. • Using innovative computer-vision and image processing techniques to automatically detect and track keypoints on the face during speech production in videos, thus allowing more natural articulation than previous sensor-based approaches. • Measuring multi-dimensional and dynamic facial movements by extracting time-related, distance-related and kinematics-related features in speech production. • Adopting the novel random forest classification approach to determine and rank the significance of facial features toward accurate speech sound categorization.
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Keyword:
Computer Science
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URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393529/ https://doi.org/10.1016/j.mex.2020.101006
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Open Agile text mining for bioinformatics: the PubAnnotation ecosystem
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Diverse Education Based on Specific Conditions in Rural Areas of China
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In: Studies in Literature and Language; Vol 19, No 3 (2019): Studies in Literature and Language; 92-95 ; 1923-1563 ; 1923-1555 (2019)
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Additional file 1: of Whole-genome sequencing and analysis of Plasmodium falciparum isolates from China-Myanmar border area ...
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Additional file 1: of Whole-genome sequencing and analysis of Plasmodium falciparum isolates from China-Myanmar border area ...
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Cross-modal Association between Auditory and Visuospatial Information in Mandarin Tone Perception in Noise by Native and Non-native Perceivers
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Cross-modal Association between Auditory and Visuospatial Information in Mandarin Tone Perception in Noise by Native and Non-native Perceivers
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Effects of acoustic and linguistic experience on Japanese pitch accent processing
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fMRI evidence for cortical modification during learning of Mandarin lexical tone
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The influence of visual speech information on the intelligibility of English consonants produced by non-native speakers
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