1,549 research outputs found
Robust Modeling of Epistemic Mental States
This work identifies and advances some research challenges in the analysis of
facial features and their temporal dynamics with epistemic mental states in
dyadic conversations. Epistemic states are: Agreement, Concentration,
Thoughtful, Certain, and Interest. In this paper, we perform a number of
statistical analyses and simulations to identify the relationship between
facial features and epistemic states. Non-linear relations are found to be more
prevalent, while temporal features derived from original facial features have
demonstrated a strong correlation with intensity changes. Then, we propose a
novel prediction framework that takes facial features and their nonlinear
relation scores as input and predict different epistemic states in videos. The
prediction of epistemic states is boosted when the classification of emotion
changing regions such as rising, falling, or steady-state are incorporated with
the temporal features. The proposed predictive models can predict the epistemic
states with significantly improved accuracy: correlation coefficient (CoERR)
for Agreement is 0.827, for Concentration 0.901, for Thoughtful 0.794, for
Certain 0.854, and for Interest 0.913.Comment: Accepted for Publication in Multimedia Tools and Application, Special
Issue: Socio-Affective Technologie
Chapter From the Lab to the Real World: Affect Recognition Using Multiple Cues and Modalities
Interdisciplinary concept of dissipative soliton is unfolded in connection with ultrafast fibre lasers. The different mode-locking techniques as well as experimental realizations of dissipative soliton fibre lasers are surveyed briefly with an emphasis on their energy scalability. Basic topics of the dissipative soliton theory are elucidated in connection with concepts of energy scalability and stability. It is shown that the parametric space of dissipative soliton has reduced dimension and comparatively simple structure that simplifies the analysis and optimization of ultrafast fibre lasers. The main destabilization scenarios are described and the limits of energy scalability are connected with impact of optical turbulence and stimulated Raman scattering. The fast and slow dynamics of vector dissipative solitons are exposed
Children\u27s Sensitivity to Pitch Variation in Language
Children acquire consonant and vowel categories by 12 months, but take much longer to learn to interpret perceptible variation. This dissertation considers childrenâs interpretation of pitch variation. Pitch operates, often simultaneously, at different levels of linguistic structure. English-learning children must disregard pitch at the lexical levelâsince English is not a tone languageâwhile still attending to pitch for its other functions. Chapters 1 and 5 outline the learning problem and suggest ways children might solve it. Chapter 2 demonstrates that 2.5-year-olds know pitch cannot differentiate words in English. Chapter 3 finds that not until age 4â5 do children correctly interpret pitch cues to emotions. Chapter 4 demonstrates some sensitivity between 2.5 and 5 years to the pitch cue to lexical stress, but continuing difficulties at the older ages. These findings suggest a late trajectory for interpretation of prosodic variation; throughout, I propose explanations for this protracted time-course
Artificial Intelligence for Suicide Assessment using Audiovisual Cues: A Review
Death by suicide is the seventh leading death cause worldwide. The recent
advancement in Artificial Intelligence (AI), specifically AI applications in
image and voice processing, has created a promising opportunity to
revolutionize suicide risk assessment. Subsequently, we have witnessed
fast-growing literature of research that applies AI to extract audiovisual
non-verbal cues for mental illness assessment. However, the majority of the
recent works focus on depression, despite the evident difference between
depression symptoms and suicidal behavior and non-verbal cues. This paper
reviews recent works that study suicide ideation and suicide behavior detection
through audiovisual feature analysis, mainly suicidal voice/speech acoustic
features analysis and suicidal visual cues. Automatic suicide assessment is a
promising research direction that is still in the early stages. Accordingly,
there is a lack of large datasets that can be used to train machine learning
and deep learning models proven to be effective in other, similar tasks.Comment: Manuscript submitted to Arificial Intelligence Reviews (2022
Multimodaalsel emotsioonide tuvastamisel pÔhineva inimese-roboti suhtluse arendamine
VĂ€itekirja elektrooniline versioon ei sisalda publikatsiooneĂks afektiivse arvutiteaduse peamistest huviobjektidest on mitmemodaalne emotsioonituvastus, mis leiab rakendust peamiselt inimese-arvuti interaktsioonis. Emotsiooni Ă€ratundmiseks uuritakse nendes sĂŒsteemides nii inimese nĂ€oilmeid kui kakĂ”net. KĂ€esolevas töös uuritakse inimese emotsioonide ja nende avaldumise visuaalseid ja akustilisi tunnuseid, et töötada vĂ€lja automaatne multimodaalne emotsioonituvastussĂŒsteem. KĂ”nest arvutatakse mel-sageduse kepstri kordajad, helisignaali erinevate komponentide energiad ja prosoodilised nĂ€itajad. NĂ€oilmeteanalĂŒĂŒsimiseks kasutatakse kahte erinevat strateegiat. Esiteks arvutatakse inimesenĂ€o tĂ€htsamate punktide vahelised erinevad geomeetrilised suhted. Teiseks vĂ”etakse emotsionaalse sisuga video kokku vĂ€hendatud hulgaks pĂ”hikaadriteks, misantakse sisendiks konvolutsioonilisele tehisnĂ€rvivĂ”rgule emotsioonide visuaalsekseristamiseks. Kolme klassifitseerija vĂ€ljunditest (1 akustiline, 2 visuaalset) koostatakse uus kogum tunnuseid, mida kasutatakse Ă”ppimiseks sĂŒsteemi viimasesetapis. Loodud sĂŒsteemi katsetati SAVEE, Poola ja Serbia emotsionaalse kĂ”neandmebaaside, eNTERFACEâ05 ja RML andmebaaside peal. Saadud tulemusednĂ€itavad, et vĂ”rreldes olemasolevatega vĂ”imaldab kĂ€esoleva töö raames loodudsĂŒsteem suuremat tĂ€psust emotsioonide Ă€ratundmisel. Lisaks anname kĂ€esolevastöös ĂŒlevaate kirjanduses vĂ€ljapakutud sĂŒsteemidest, millel on vĂ”imekus tunda Ă€raemotsiooniga seotud Ìzeste. Selle ĂŒlevaate eesmĂ€rgiks on hĂ”lbustada uute uurimissuundade leidmist, mis aitaksid lisada töö raames loodud sĂŒsteemile ÌzestipĂ”hiseemotsioonituvastuse vĂ”imekuse, et veelgi enam tĂ”sta sĂŒsteemi emotsioonide Ă€ratundmise tĂ€psust.Automatic multimodal emotion recognition is a fundamental subject of interest in affective computing. Its main applications are in human-computer interaction. The systems developed for the foregoing purpose consider combinations of different modalities, based on vocal and visual cues. This thesis takes the foregoing modalities into account, in order to develop an automatic multimodal emotion recognition system. More specifically, it takes advantage of the information extracted from speech and face signals. From speech signals, Mel-frequency cepstral coefficients, filter-bank energies and prosodic features are extracted. Moreover, two different strategies are considered for analyzing the facial data. First, facial landmarks' geometric relations, i.e. distances and angles, are computed. Second, we summarize each emotional video into a reduced set of key-frames. Then they are taught to visually discriminate between the emotions. In order to do so, a convolutional neural network is applied to the key-frames summarizing the videos. Afterward, the output confidence values of all the classifiers from both of the modalities are used to define a new feature space. Lastly, the latter values are learned for the final emotion label prediction, in a late fusion. The experiments are conducted on the SAVEE, Polish, Serbian, eNTERFACE'05 and RML datasets. The results show significant performance improvements by the proposed system in comparison to the existing alternatives, defining the current state-of-the-art on all the datasets. Additionally, we provide a review of emotional body gesture recognition systems proposed in the literature. The aim of the foregoing part is to help figure out possible future research directions for enhancing the performance of the proposed system. More clearly, we imply that incorporating data representing gestures, which constitute another major component of the visual modality, can result in a more efficient framework
Event-Related Potentials and Emotion Processing in Child Psychopathology
In recent years there has been increasing interest in the neural mechanisms underlying altered emotional processes in children and adolescents with psychopathology. This review provides a brief overview of the most up-to-date findings in the field of Event-Related Potentials (ERPs) to facial and vocal emotional expressions in the most common child psychopathological conditions. In regards to externalising behaviour (i.e. ADHD, CD), ERP studies show enhanced early components to anger, reflecting enhanced sensory processing, followed by reductions in later components to anger, reflecting reduced cognitive-evaluative processing. In regards to internalising behaviour, research supports models of increased processing of threat stimuli especially at later more elaborate and effortful stages. Finally, in autism spectrum disorders abnormalities have been observed at early visual-perceptual stages of processing. An affective neuroscience framework for understanding child psychopathology can be valuable in elucidating underlying mechanisms and inform preventive intervention
Continuous Interaction with a Virtual Human
Attentive Speaking and Active Listening require that a Virtual Human be capable of simultaneous perception/interpretation and production of communicative behavior. A Virtual Human should be able to signal its attitude and attention while it is listening to its interaction partner, and be able to attend to its interaction partner while it is speaking â and modify its communicative behavior on-the-fly based on what it perceives from its partner. This report presents the results of a four week summer project that was part of eNTERFACEâ10. The project resulted in progress on several aspects of continuous interaction such as scheduling and interrupting multimodal behavior, automatic classification of listener responses, generation of response eliciting behavior, and models for appropriate reactions to listener responses. A pilot user study was conducted with ten participants. In addition, the project yielded a number of deliverables that are released for public access
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