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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 ADFAC: Automatic detection of facial articulatory features Garg, Saurabh; Hamarneh, Ghassan; Jongman, Allard; Sereno, Joan A.; Wang, Yue In: MethodsX (2020) 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. Keyword: Computer Science URL: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7393529/ https://doi.org/10.1016/j.mex.2020.101006 BASE Hide details 2 ADFAC: Automatic detection of facial articulatory features Garg, Saurabh; Hamarneh, Ghassan; Jongman, Allard. - : Elsevier, 2020 BASE Show details 3 Dual Language Models for Code Switched Speech Recognition ... Garg, Saurabh; Parekh, Tanmay; Jyothi, Preethi. - : arXiv, 2017 BASE Show details 4 A generalised framework for script identification Joshi, Gopal Datt; Garg, Saurabh; Sivaswamy, Jayanthi In: International journal on document analysis and recognition. - Berlin ; Heidelberg : Springer 10 (2007) 2, 55-68 BLLDB OLC Linguistik Show details 5 Issues in Corpus Development for Multi-Party Multi-Modal Task-Oriented Dialogue Robinson, Susan; Martinovski, Bilyana; Garg, Saurabh... In: DTIC (2006) BASE Show details 6 Evaluation of Transcription and Annotation Tools for a Multi-Modal, Multi-Party Dialogue Corpus Garg, Saurabh; Martinovski, Bilyana; Robinson, Susan... In: DTIC (2004) BASE Show details

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