AMI Research
The AMI team has expertise and experience in speech recognition, educational assessment, product development, machine learning, automated scoring, and psychometrics. Below you will find a collection of key presentations and research articles authored by AMI team members.
SkillCheck
AMI, as part of a Small Business Innovation Research (SBIR) grant from the Institute of Education Sciences of the U.S. Department of Education is developing and commercializing SkillCheck, a new innovative, artificial intelligence (AI) based, analytical technology for reading. Designed for students in grades 1 through 4, SkillCheck is an automated diagnostic system of oral reading fluency (ORF) performance and an intelligent practice tool to improve foundational reading skills to achieve fluent oral reading. This Phase 2 project follows the successful completion of a R&D feasibility study funded by a SBIR grant where AMI demonstrated the technical feasibility of the product.
In short, SkillCheck will provide better diagnostic information for teachers to help address the skills students need to become fluent readers without additional testing. For students, SkillCheck will provide engaging practice opportunities to enable them to become confident, fluent readers.
Virtual Interview Practice Partner (VIPP) Product Concept
This overview outlines AMI's product concept and roadmap for the Virtual Interview Practice Partner (VIPP). The VIPP mobile application is an automated interview practice tool and a self-assessment focusing on hard-to-measure communication skills and situational judgement tasks. Students can prepare for competitive job interviews and practice how to demonstrate their professional communication, collaboration, and problem-solving skills in ways employers value. VIPP extends spoken language technology within interactive task simulations, including situational judgement tasks, built and validated by AMI with National Science Foundation funding.
Team Papers and Presentations
Bernstein, J., Suzuki, M., Cheng, J., Yang C., Ciancio, D., Brenner, D. (2020). Automated Estimation of Foundational Reading Skills from Recorded Passage Read-Alouds. California Educational Research Association (CERA) 99th Annual Conference, Anaheim, CA. View Slides
Cheng, S., Cohen A., Holmlund T., Foltz, P., Cheng J., Bernstein J., Rosenfeld E., Elvev åg B. (2020). A dynamic method, analysis, and model of short-term memory for serial order with clinical applications, Psychiatry Research, doi: https://doi.org/10.1016/j.psychres.2020.113494 View Article
Bernstein, J., Cheng, J., Holmlund, T., Rosenfield, E. & Massaro, D. (2020). Automated measures of spoken language for monitoring and diagnosis. Behavioural Measures of Language Acquisition 2020, Venice, Italy. Watch Video
Bernstein, J., Cheng, J., Balogh, J., & Downey, R. (2020). Artificial intelligence for scoring oral reading fluency. In H. Jiao & R. Lissitz (Eds.), Applications of artificial intelligence to assessment. Charlotte, NC: Information Age Publisher. View Chapter
Suzuki, M., Bernstein, J., Cheng, J., & Okuda, T. (2019). Accurate reading rate: Validations of machine scoring. The 178 Meeting of the Acoustical Society of America, San Diego, CA. View Poster
Bernstein, J., Cheng, J., Magooda, A. (2018). Perseverance measured in children’s oral reading. The 176 Meeting of the Acoustical Society of America, Vancouver, BC. View Poster
Cheng, J. (2018). Real-time scoring of an oral reading assessment on mobile devices. Proceedings of Interspeech 2018, 1621-1625. View Article
Bernstein, J., Sabatini, J., Balogh, J., & Cheng, J. (2017). Oral reading assessment: Leveled fluency, self-administered and automatically scored. California Educational Research Association (CERA) 96th Annual Conference. View Article
Cheng, J., Chen, X., & Metallinou, A. (2015). Deep neural network acoustic models for spoken assessment applications. Speech Communication, 73, 14–27. View Article
Ramachandran, L., Cheng, J., & Foltz, P. (2015). Identifying patterns for short answer scoring using graph-based lexico-semantic text matching. Proceedings of the Tenth Workshop on Innovative Use of NLP for Building Education Applications, 97–106. View Article
Cheng, J., D'Antilio, Y. Z., Chen, X., & Bernstein, J. (2014). Automatic assessment of the speech of young English learners. Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications, 12–21. View Article
Metallinou, A. & Cheng, J. (2014). Using deep neural networks to improve proficiency assessment for children English language learners. Proceedings of Interspeech 2014, 1468-1472. View Article
Bernstein, J., Todic, O., Neumeyer, K., Schultz, K., & Zhao, L. (2013). Young children’s performance on self-administered iPad language activities. Proceedings of SLaTE 2013, 24-25. View Article
Balogh, J., Bernstein, J., Cheng, J., Van Moere, A., Townshend, B., & Suzuki, M (2012). Validation of automated scoring of oral reading. Educational & Psychological Measurement, 72(3), 435-452. View Article
Bernstein, J. (2012). Computer scoring of spoken responses. In Chapelle, C.A. (Ed.), The encyclopedia of applied linguistics. Oxford: Willey-Blackwell. View Chapter
Cheng, J. (2011). Automatic assessment of prosody in high-stakes English tests. Interspeech-2011, 1589-1592. View Article
Bernstein, J. & Cheng, J. (2008). Logic and validation of a fully automatic spoken English test. In V.M. Holland & F.P. Fisher (Eds.), Speech technologies for language learning. Lisse, NL: Swets & Zeitlinger. View Chapter
Balogh, J. & Bernstein, J. (2007). Workable models of standard performance in English and Spanish. In Y. Matsumoto, D.Y. Oshima, O.R. Robinson, & P. Sells (Eds.), Diversity in language: Perspective and implications (pp. 20-41). Stanford, CA: Center for the Study of Language and Information Publications. View Chapter
Rosenfeld, E., Massaro, D., & Bernstein, J. (2003). Automatic analysis of vocal manifestations of apparent mood or affect. Proceedings of the Third International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, 5-8. View Article
Cheng, J. & Townshend, B. (2009). A rule-based language model for reading recognition. Proceedings of the SLaTE-2009 workshop, 33-36. View Article
Cheng, J. & Druzdzel, M.J. (2000). AIS-BN: An adaptive importance sampling algorithm for evidential reasoning in large Bayesian networks. Journal of Artificial Intelligence Research, 13, 155-188. View Article
Ehsani, F., Bernstein, J., & Najmi, A. (2000). An interactive dialog system for learning Japanese. Speech Communication, 30(2-3), 167-177. View Article