Automatic Evaluation System of English Prosody Based on Word Importance Factor

Abstract

Prosody plays an important role in speech communication between humans. Although several computer-assisted language learning (CALL) systems with utterance evaluation function have been developed, the accuracy of their prosody evaluation is still poor. In the present paper, we develop new methods by which to evaluate the rhythm and intonation of English sentences uttered by Japanese learners. The novel features of our study are as follows: (1) new prosodic features are added to traditional features, and (2) word importance factors are introduced in the calculation of intonation score. The word importance factor is automatically estimated using the ordinary least squares method and is optimized based on word clusters generated by a decision tree. Experiments conducted herein reveal the correlation coefficient (±1.0 denotes the best correlation) between the rhythm score given by native speakers and the system was -0.55. In contrast, a conventional feature (pause insertion error rate) gave a correlation coefficient of only -0.11. The correlation coefficient between the intonation scores given by native speakers and the system was only -0.29. However, the word importance factor with decision tree clustering improved the correlation coefficient to 0.45. In addition, we propose a method of integrating the rhythm score with the intonation score, which improved the correlation coefficient from 0.45 to 0.48 for evaluating intonation.

Keywords

• prosody evaluation
• intonation
• rhythm
• decision tree
• computer-assisted language learning system