Most previous research on automated speech scoring has focused on restricted, predictable speech. For automated scoring of unrestricted spontaneous speech, speech proficiency has been evaluated primarily on aspects of pronunciation, fluency, vocabulary and language usage but not on aspects of content and topicality. In this paper, we explore features representing the accuracy of the content of a spoken response. Content features are generated using three similarity measures, including a lexical matching method (Vector Space Model) and two semantic similarity measures (Latent Semantic Analysis and Pointwise Mutual Information). All of the features exhibit moderately high correlations with human proficiency scores on human speech transcriptions. The correlations decrease somewhat due to recognition errors when evaluated on the output of an automatic speech recognition system; however, the additional use of word confidence scores can achieve correlations at a similar level as for human transcriptions.
For example, REF used LSA and Pairwise Mutual Information approaches to evaluate the content correctness of unrestricted spontaneous spoken responses.
Exploring Content Features for Automated Speech Scoring
NAACL
Computer Science