Converging technological advances in sensing, machine learning and computing offer tremendous opportunities for continuous contextually rich yet unobtrusive multimodal, spatiotemporal characterization of a child’s behavior, communication and interaction, across stages of development.  This in turn promises novel possibilities for understanding and supporting various aspects of child-centered applications from health and well-being to learning and entertainment.

Recent approaches that have leveraged judicious use of both data and knowledge have yielded significant advances in this regard, for example in deriving rich, context-aware information from multimodal biobehavioral signal sources including human speech, language, and videos of behavior as well as physiological information. This talk will focus on some of the advances, opportunities and challenges in gathering such data and creating algorithms for machine processing of such cues in a child centric setting.  It will highlight some of our ongoing efforts in behavioral machine intelligence drawing examples from the domain of Autism Spectrum Disorder.

Short Bio: Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California, where he is Professor of Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology—Head & Neck Surgery, Director of the Ming Hsieh Institute and Research Director of the Information Sciences Institute. Prior to USC he was with AT&T Bell Labs and AT&T Research. His research focuses on human-centered information processing and communication technologies.  He is a 2022 Guggenheim Fellow and a Fellow of the National Academy of Inventors, the Acoustical Society of America, IEEE, ISCA, the American Association for the Advancement of Science (AAAS), the Association for Psychological Science, and the American Institute for Medical and Biological Engineering (AIMBE).  He is a recipient of several honors including the 2015 Engineers Council’s Distinguished Educator Award, a Mellon award for mentoring excellence, the 2005 and 2009 Best Journal Paper awards from the IEEE Signal Processing Society and serving as its Distinguished Lecturer for 2010-11, a 2018 ISCA CSL Best Journal Paper award, and serving as an ISCA Distinguished Lecturer for 2015-16, Willard R. Zemlin Memorial Lecturer for ASHA in 2017, and the Ten Year Technical Impact Award in 2014 and the Sustained Accomplishment Award in 2020 from ACM ICMI. He has published over 900 papers and has been granted eighteen U.S. patents. His research and inventions have led to technology commercialization including through startups he co-founded: Behavioral Signals Technologies focused on the telecommunication services and AI based conversational assistance industry and Lyssn focused on mental health care delivery, treatment and quality assurance.

Reading tutors that incorporate Automatic Speech Recognition (ASR) technology have been proposed as valuable educational software that can provide additional practice and support in reading aloud. Most systems available so far have been mainly used to follow children while they read aloud so that support can be provided to indicate the correct form of the word when difficulties arise.   In our own research, we have investigated the use of ASR technology at earlier stages of learning to read, when children are still in the process of developing decoding skills. This requires specialized algorithms that can detect reading errors at a more detailed level. In addition, our Reading tutor is equipped with logging capabilities that allow to record what happens during reading practice. This provides a considerable amount of data that can be used to improve the ASR technology and to finetune the system, but also to gain more detailed insights into how the process of learning to read unfolds.  

Short Bio: Dr. Catia Cucchiarini holds a PhD from Radboud University, where she is now Principal Investigator in the research group Language and Speech, Learning and Therapy and the Centre for Language and Speech Technology. She worked at the Centre for Language and Migration of KU Leuven in Belgium and is a Senior Advisor at the Dutch-Flemish intergouvernemntal policy organization The Union for the Dutch Language (Taalunie) in the Hague.  She conducted research on  phonetic transcription, speech processing, speech and language resources, language learning, idiomatic language and speech technology applications in Computer Assisted Language Learning and e-health, for which she received several national and international grants. She is a member of the editorial board of Computer Assisted Language Learning, and of the ‘International Speech Communication Association’ (ISCA) ‘Special Interest Group’ (SIG) on ‘Speech and Language Technology in Education’.