Abstract

Automatic literacy assessment technology can help children acquire reading skills by providing teachers valuable feedback in a repeatable, consistent manner. Recent research efforts have concentrated on detecting mispronunciations during word-reading and sentence-reading tasks. These token-level assessments are important since they highlight specific errors made by the child. However, there is also a need for more high-level automatic assessments that capture the overall performance of the children. These high-level assessments can be viewed as an interpretive extension to token-level assessments, and may be more perceptually relevant to teachers and helpful in tracking performance over time. In this paper, we model and predict the overall reading ability of young children reading a list of English words aloud. The data consist of audio recordings, collected in real kindergarten to second grade …

Date

September 20, 2010

Authors

Matthew P Black, Joseph Tepperman, Shrikanth S Narayanan

Journal

IEEE Transactions on Audio, Speech, and Language Processing

Volume

19

Issue

4

Pages

1015-1028

Publisher

IEEE