Improving L2 English Rhythm Evaluation with Automatic Sentence Stress Detection

Abstract

English is a stress-timed language, for which sentence stress or prosodic stress plays an important role. It’s then difficult for Chinese who are used to the syllable-timed rhythm to learn the rhythm of English [1]. In this paper, we investigate how to improve the rhythm evaluation based on the sentence stress for Chinese who learn English as a second language (ESL). Particularly, we explore some rhythm measures to quantify rhythmic differences among second language (L2) learners based on sentence stress. To relieve the dependency on labeled data of sentence stress, we predict sentence stress automatically utilizing a hierarchical network with bidirectional Long Short-Term Memory (BLSTM) [2]. We evaluate the proposed method based on the corpus consisting of 3,500 sentences recorded by 100 Chinese speakers aging from 10 to 20 years old, which was marked with the sentence stress labels and scored by three experts. Experimental results show the proposed sentence stress measure is well correlated with labeled prosody scores with a correlation coefficient of −0.73 and the automatic labeling method achieves comparable results with the method with gold labels.