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 $f_0$, 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.

韵律特征（例如词汇和短语应力）是第二语言（L2）讲者学习中最具挑战性的特征之一。量化语言学习者在韵律方面的能力的能力可用于语言教师并改善对第二语言能力的评估。但是，自动评估需要对韵律特征进行可靠的自动分析，以便对L2语音和参考样本的产生进行比较。在本文中，我们研究了基于信号的音节突出是否可以用来预测芬兰瑞典学习者的韵律能力。通过使用以下项的组合的连续小波变换分析，估计了180个L2和45个自然（L1）语音的音节水平突出。$F_0$，能量和持续时间。L2话语由四位专家评估人员使用修订后的CEFR量表对韵律特征进行分级。L2话语的突出估计值与L1话语和语言压力模式的估计值之间的相关性用作衡量L2说话者的韵律水平的指标。结果表明，以这种方式概念化的协议水平与专家评分者的评估显着相关，这为在基于计算机的L2口语能力评估中使用基于小波的突出估计技术提供了有力的支持。