Caimber Research Projects

Caimber has expertise 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 members of the Caimber team.

Papers and Presentations

Bernstein, J. (January 2022).
Machine learning methods behind AI applications in Education.
Annual IES Principal Investigators Meeting: Advancing Education and Inclusion in the Education Sciences.
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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.
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Cheng, S., Cohen A., Holmlund T., Foltz, P., Cheng J., Bernstein J., Rosenfeld E., Elvevåg B. (2020).
A dynamic method, analysis, and model of short-term memory for serial order with clinical applications.
Psychiatry Research, doi:
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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.
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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.
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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.
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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.
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Cheng, J. (2018).
Real-time scoring of an oral reading assessment on mobile devices.
Proceedings of Interspeech 2018, 1621-1625.
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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.
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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.
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Cheng, J., Chen, X., & Metallinou, A. (2015).
Deep neural network acoustic models for spoken assessment applications.
Speech Communication, 73, 14–27.
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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.
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Metallinou, A. & Cheng, J. (2014).
Using deep neural networks to improve proficiency assessment for children English language learners.
Proceedings of Interspeech 2014, 1468-1472.
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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.
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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.
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Bernstein, J. (2012).
Computer scoring of spoken responses.
In Chapelle, C.A. (Ed.), The encyclopedia of applied linguistics. Oxford: Willey-Blackwell.
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Cheng, J. (2011).
Automatic assessment of prosody in high-stakes English tests.
Interspeech-2011, 1589-1592.
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J. Bernstein, A. Van Moere & J. Cheng (2010)
Validating automated speaking tests.
Language Testing, 27(3) 355-377.
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Cheng, J. & Townshend, B. (2009).
A rule-based language model for reading recognition.
Proceedings of the SLaTE-2009 workshop, 33-36.
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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.
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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.
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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.
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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.
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Ehsani, F., Bernstein, J., & Najmi, A. (2000).
An interactive dialog system for learning Japanese.
Speech Communication, 30(2-3), 167-177.
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