Improving Automated Scoring of Prosody of ORF with Between-Word Silence Features

• In Event: GSIC Poster Session I

Abstract

This study aimed to use between-word silence times and acoustic features for the automated scoring of prosody in oral reading fluency assessment. The results demonstrated that features related to between-word silence time helped improve the classifier's performance with Elastic Net regularization.

Authors

• Kuo Wang, Southern Methodist University

• Akihito Kamata, SOUTHERN METHODIST UNIVERSITY

• Yusuf Kara, UNIVERSITY OF MIAMI

• Joseph F. T. Nese, UNIVERSITY OF OREGON