Abstract

Giving feedback on the degree of nativeness of a student's speech is an important aspect of computer-aided language learning. This task has been addressed by many studies focusing on the segmental assessment of the speech signal. To better model human nativeness scores, other aspects of speech should also be considered, such as prosody. This study examines the use of prosodic information to evaluate the degree of nativeness of student pronunciation, independent of the text. Supervised strategies based on human grades are used in an attempt to select promising features for this task. Previous results obtained with non-native speakers showed improvements in the correlation between human and automatic scores. New strategies were evaluated with tests including native and non-native speakers. Specific features based on durations, namely for intra-sentence pauses, revealed potential use for further improvements.

Citations