Abstract

To automatically assess young children’s reading skills as demonstrated by isolated words read aloud, we propose a novel structure for a Bayesian Network classifier. Our network models the generative story among speech recognition-based features, treating pronunciation variants and reading mistakes as distinct but not independent cues to a qualitative perception of reading ability. This Bayesian approach allows us to estimate the probabilistic dependencies among many highly-correlated features, and to calculate soft decision scores based on the posterior probabilities for each class. With all proposed features, the best version of our network outperforms the C4. 5 decision tree classifier by 17% and a Naive Bayes classifier by 8%, in terms of correlation with speaker-level reading scores on the Tball data set. This best correlation of 0.92 approaches the expert inter-evaluator correlation, 0.95.

Date

2007

Authors

Joseph Tepperman, Matthew Black, Patti Price, Sungbok Lee, Abe Kazemzadeh, Matteo Gerosa, Margaret Heritage, Abeer Alwan, Shrikanth S Narayanan

Conference

INTERSPEECH

Pages

2185-2188