Abstract

To automate assessments of beginning readers, especially those still learning English, we have investigated the types of knowledge sources that teachers use and have tried to incorporate them into an automated system. We describe a set of speech recognition and verification experiments and compare teacher scores with automatic scores in order to decide when a novel pronunciation is best viewed as a reading error or as dialect variation. Since no one classroom teacher is expected to be familiar with as many dialect systems as might occur in an urban classroom, making progress in automated assessments in this area can improve the consistency and fairness of reading assessment. We found that automatic methods performed best when the acoustic models were trained on both native and non-native speech, and argue that this training condition is necessary for automatic reading assessment since a child’s …

Date

2009

Authors

Patti Price, Joseph Tepperman, Markus Iseli, Thao Duong, Matthew Black, Shizhen Wang, Christy Kim Boscardin, Margaret Heritage, P David Pearson, Shrikanth Narayanan, Abeer Alwan

Journal

Speech Communication

Volume

51

Issue

10

Pages

968-984

Publisher

North-Holland