Using Ontology-based Approaches to Representing Speech Transcripts for Automated Speech Scoring

This paper presents a thesis proposal on approaches to automatically scoring non-native speech from second language tests. Current speech scoring systems assess speech by primarily using acoustic features such as fluency and pronunciation; however content features are barely involved. Motivated by this limitation, the study aims to investigate the use of content features in speech scoring systems. For content features, a central question is how speech content can be represented in appropriate means to facilitate automated speech scoring. The study proposes using ontology-based representation to perform concept level representation on speech transcripts, and furthermore the content features computed from ontology-based representation may facilitate speech scoring. One baseline and two ontology-based representations are compared in experiments. Preliminary results show that ontology-based representation slightly improves performance of one content feature for automated scoring over the baseline system.