Illinois Institute of Technology
Adjunct Professor  Computer Science
Blue Cross and Blue Shield of Illinois, Montana, New Mexico, Oklahoma & Texas
Senior Director of Data Science  Enterprise Analytics Coe
American Family Insurance Aug 2016 - Mar 2019
Data Science Manager  Identification and Adoption of Machine Learning and Data Science Methods
Civis Analytics Apr 2014 - Jul 2016
Lead Data Scientist  Complete Ownership of Unstructured Data Strategy, Processes and Implementation
Educational Testing Service (Ets) Feb 2003 - Apr 2014
Director, Nlp and Speech Research  Group Leader  Research Scientist
Education:
University of Chicago 1996 - 2002
Doctorates, Doctor of Philosophy, Linguistics
University of Chicago 1992 - 1996
Bachelors, Bachelor of Arts, Linguistics
Freie Universität Berlin 1995 - 1995
Missouri State University
Skills:
Statistics Computational Linguistics Natural Language Processing Machine Learning Research Data Mining Artificial Intelligence Text Mining Linguistics Information Retrieval Computer Science Python Semantics Analysis Latex Science Educational Technology Higher Education Data Visualization Data Analysis Teaching
- Princton NJ, US Derrick Higgins - Chicago IL, US Klaus Zechner - Princeton NJ, US Shasha Xie - Sunnyvale CA, US Je Hun Jeon - Woburn MA, US Keelan Evanini - Pennington NJ, US Guangming Ling - Lawrenceville NJ, US
International Classification:
G10L 15/08 G10L 15/00
Abstract:
A method for scoring non-native speech includes receiving a speech sample spoken by a non-native speaker and performing automatic speech recognition and metric extraction on the speech sample to generate a transcript of the speech sample and a speech metric associated with the speech sample. The method further includes determining whether the speech sample is scorable or non-scorable based upon the transcript and speech metric, where the determination is based on an audio quality of the speech sample, an amount of speech of the speech sample, a degree to which the speech sample is off-topic, whether the speech sample includes speech from an incorrect language, or whether the speech sample includes plagiarized material. When the sample is determined to be non-scorable, an indication of non-scorability is associated with the speech sample. When the sample is determined to be scorable, the sample is provided to a scoring model for scoring.