Automatic Rating of Spontaneous Speech for Low-Resource Languages

Abstract

Automatic spontaneous speaking assessment systems bring numerous advantages to second language (L2) learning and assessment such as promoting self-learning and reducing language teachers' workload. Conventionally, these systems are developed for languages with a large number of learners due to the abundance of training data, yet languages with fewer learners such as Finnish and Swedish remain at a disadvantage due to the scarcity of required training data. Nevertheless, recent advancements in self-supervised deep learning make it possible to develop automatic speech recognition systems with a reasonable amount of training data. In turn, this advancement makes it feasible to develop systems for automatically assessing spoken proficiency of learners of underresourced languages: L2 Finnish and Finland Swedish. Our work evaluates the overall performance of the L2 ASR systems as well as the the rating systems compared to human reference ratings for both languages.