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 reading ability is not directly observable in one utterance. We also found assessment of emerging reading skills in young children to be an area ripe for more research!
['Patti Price', 'Joseph Tepperman', 'Markus Iseli', 'Thao Duong', 'Matthew P. Black', 'Shizhen Wang', 'Christy Boscardin', 'P. David Pearson', 'Shrikanth Narayanan', 'Abeer Alwan']
Assessment of emerging reading skills in young native speakers and language learners