@inproceedings{valenti2020multilevel, title = {A Multi-level Framework for Understanding Spoken Dialogue Using Topic Detection}, author = {Andrew Valenti and Ravenna Thielstrom and Michael Gold and Felix Gervits and Matthias Scheutz}, booktitle = {Proceedings of the 24th Workshop on the Semantics and Pragmatics of Dialogue (SemDial)}, year = {2020}, topics={hri, dia, speech}, projects={diarc}, }

@InProceedings{Valenti2020a, author = {Andrew P. Valenti and Avram Bock and Meia Chita-Tegmark and Michael Gold and Matthias Scheutz}, year = {2020}, topics = {hri, learn, neural}, projects = {cm, aa}, title = {Emotion Expression in a Socially Assistive Robot for Persons with {P}arkinson's disease}, booktitle={{13th PErvasive Technologies Related to Assistive Environments Conference (PETRA ’20)}}, organization={ACM}, address = {Corfu, Greece}, month = {July}, synopsis={Emotions are crucial for human social interactions and thus people communicate emotions through a variety of modalities: kinesthetic (through facial expressions, body posture and gestures), auditory (the acoustic features of speech) and semantic (the content of what they say). Sometimes however, communication channels for certain modalities can be unavailable (\eg in the case of texting), and sometimes they can be compromised, due to a disorder such as Parkinson's disease (PD) that may affect facial, gestural and speech expressions of emotions. To address this, we developed a prototype for an emoting robot that can detect emotions in one modality, specifically in the content of speech, and then express them in another modality, specifically through gestures. This paper is part of a larger project to develop a prototype for a socially assistive robot for PD persons. The goal is to present the technical implementation of one robot capability: emotion expression.}, }

@Article{valenti19at, author = {Andrew P. Valenti and Meia Chita-Tegmark and Linda Tickle-Degnen and Alexander W. Bock and Matthias J. Scheutz}, title = {Using topic modeling to infer the emotional state of people living with {P}arkinson's disease}, journal = {Assistive Technology}, pages = {1--10}, year = {2019}, publisher = {Taylor and Francis}, URL = {https://doi.org/10.1080/10400435.2019.1623342}, topics = {hri, learn, neural}, projects = {cm, aa}, synopsis ={We describe the initial development stage of a robot companion that can assist the communication and }

@InProceedings{valenti19aamasws, author = {Andrew P. Valenti and Meia Chita-Tegmark and Theresa Law and Alexander W. Bock and Bradley Oosterveld and Matthias Scheutz}, year = {2019}, title = {When your face and tone of voice don't say it all: Inferring emotional state from word semantics and conversational topics}, booktitle = {Workshop on Cognitive Architectures for {HRI}: Embodied Models of Situated Natural Language Interactions at {AAMAS 2019}}, address = {Montreal, Canada}, month = {May}, topics = {hri, learn, neural}, projects = {ade, cm, aa}, synopsis = {We describe the initial development stage of a cognitive robotic architecture that can assist the communication and detection of emotions in interactions where some modalities are totally or partially compromised. We hypothesize that the distribution of topics extracted from each sentence, that is part of a collection of written text documents, using the Latent Dirichlet Allocation (LDA) generative model can be associated with measures of emotional valence and arousal. We integrated our model into the cognitive robotic architecture, DIARC, and demonstrated how a robot can use speech transcriptions to detect positive or negative emotion valence and express it through its facial features.}, }

@InProceedings{valentiCCN2017CCNNeural, title={A Neural Field Model of Sequence Perception}, author={Andrew Valenti and Bradley Oosterveld and Matthias Scheutz}, booktitle={Proceeding of Cognitive Computational Neuroscience Conference 2017}, year={2017}, projects={ccm}, topics={speech,vision,learn}, video={http://hrilab.tufts.edu/movies/demodnftimit.mpeg}, synopsis={We demonstrate how perception arises when the model learns to decode a sequence represented as a pattern of neural field activation, representing any mode of sensory input.}, }

@InProceedings{valenti2016cogsciNeural, title={A Neural Field Model of Word Repetition effects in Early Time-Course ERPs in Spoken Word Perception}, author={Andy Valenti and Michael Brady and Matthias Scheutz and Phillip Holcomb and He Pu}, booktitle={Proceedings of Cog Sci 2016}, year={2016}, projects={ccm}, topics={speech,learn,neural}, synopsis={We demonstrate the dynamics of the model evolve to generate an event-related potential (ERP) associate with word repetition in speech and fit the waveform to human experimental data}, }

@InProceedings{valentischeutz13iccm, title = {A computational model of bilingual inhibitory control in a lexical decision task}, author = {Andrew Valenti and Matthias Scheutz}, booktitle = {International Conference on Cognitive Modeling}, year = {2013}, projects={ccm}, topics={neural}, synopsis={We model a cognitive process that is hypothesized to explain the language switching cost incurred by bilingual subjects when performing a lexical decision task}, }