2023

Al-Ghezi, Ragheb; Voskoboinik, Ekaterina; Getman, Yaroslav; Von Zansen, Anna; Kallio, Heini; Kurimo, Mikko; Huhta, Ari; Hildén, Raili

The development of automated systems for evaluating spontaneous speech is desirable for L2 learning, as it can be used as a facilitating tool for self-regulated learning, language proficiency assessment, and teacher training programs. However, languages with fewer learners face challenges due to the scarcity of training data. Recent advancements in machine learning have made it possible to develop systems with a limited amount of target domain data. To this end, we propose automatic speaking assessment systems for spontaneous L2 speech in Finnish and Finland Swedish, comprising six machine learning models each, and report their performance in terms of statistical evaluation criteria.