Name
Abstract | ||
|---|---|---|
This paper presents a novel student model intended to automate word-list-based reading assessments in a classroom setting, specifically for a student population that includes both native and nonnative speakers of English. As a Bayesian Network, the model is meant to conceive of student reading skills as a conscientious teacher would, incorporating cues based on expert knowledge 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 knowledge) achieve 0.7 correlation of their automatic scores with human assessments on the TBALL dataset. Our proposed structure significantly outperforms this baseline, and a simpler data-driven structure achieves 0.87 correlation through the use of novel features, surpassing the lower range of inter-annotator agreement. Scores estimated by this new model are also shown to exhibit the same biases along demographic lines as human listeners. Though used here for reading assessment, this model paradigm could be used in other pedagogical applications like foreign language instruction, or for inferring abstract cognitive states like categorical emotions. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/TASL.2010.2047812 | Audio, Speech, and Language Processing, IEEE Transactions |
Keywords | Field | DocType |
scoring word reading skills,novel student model,generative student model,new model,word-list-based reading assessment,proposed structure,student reading skill,student population,prior knowledge,model paradigm,simpler data-driven structure,expert knowledge,pediatrics,bayesian network,decoding,natural language processing,speech processing,correlation,bayesian networks,speech,bayesian methods | Pronunciation,Population,Categorical variable,Computer science,Speech recognition,Bayesian network,Natural language processing,Artificial intelligence,Generative grammar,Cognition,Foreign language,Bayesian probability | Journal |
Volume | Issue | ISSN |
19 | 2 | 1558-7916 |
Citations | PageRank | References |
6 | 0.50 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Joseph Tepperman | 1 | 73 | 8.59 |
| Sungbok Lee | 2 | 1394 | 84.13 |
| Narayanan Shrikanth | 3 | 5558 | 439.23 |
| A. Alwan | 4 | 59 | 4.35 |