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348 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2011 A Generative Student Model for Scoring
 

Summary: 348 IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. 19, NO. 2, FEBRUARY 2011
A Generative Student Model for Scoring
Word Reading Skills
Joseph Tepperman, Member, IEEE, Sungbok Lee, Member, IEEE, Shrikanth (Shri) Narayanan, Senior Member, IEEE,
and Abeer Alwan, Fellow, IEEE
Abstract--This paper presents a novel student model intended to
automate word-list-based reading assessments in a classroom set-
ting, specifically for a student population that includes both na-
tive and nonnative speakers of English. As a Bayesian Network,
the model is meant to conceive of student reading skills as a consci-
entious teacher would, incorporating cues based on expert knowl-
edge of pronunciation variants and their cognitive or phonological
sources, as well as prior knowledge of the student and the test itself.
Alongside a hypothesized structure of conditional dependencies,
we also propose an automatic method for refining the Bayes Net
to eliminate unnecessary arcs. Reading assessment baselines that
use strict pronunciation scoring alone (without other prior knowl-
edge) achieve 0.7 correlation of their automatic scores with human
assessments on the TBALL dataset. Our proposed structure signif-
icantly outperforms this baseline, and a simpler data-driven struc-

  

Source: Alwan, Abeer - Electrical Engineering Department, University of California at Los Angeles

 

Collections: Computer Technologies and Information Sciences