Analyzing second language proficiency using wavelet-based prominence estimates

Abstract

Prosodic characteristics, such as lexical and phrasal stress, are one of the most challenging features for second language (L2) speakers to learn. The ability to quantify language learners’ proficiency in terms of prosody can be of use to language teachers and improve the assessment of L2 speaking skills. Automatic assessment, however, requires reliable automatic analyses of prosodic features that allow for the comparison between the productions of L2 speech and reference samples. In this paper we investigate whether signal-based syllable prominence can be used to predict the prosodic competence of Finnish learners of Swedish. Syllable-level prominence was estimated for 180 L2 and 45 native (L1) utterances by a continuous wavelet transform analysis using combinations of f0, energy, and duration. The L2 utterances were graded by four expert assessors using the revised CEFR scale for prosodic features. Correlations of prominence estimates for L2 utterances with estimates for L1 utterances and linguistic stress patterns were used as a measure of prosodic proficiency of the L2 speakers. The results show that the level of agreement conceptualized in this way correlates significantly with the assessments of expert raters, providing strong support for the use of the wavelet-based prominence estimation techniques in computer-assisted assessment of L2 speaking skills.