The development of large multilingual speech models provides the possibility to construct high-quality speech technology even for low-resource languages. In this paper, we present the speech data of L2 learners of Finnish and Finland Swedish that we have recently collected for training and evaluation of automatic speech recognition (ASR) and speaking assessment (ASA). It includes over 4000 recordings by over 300 students per language in short read-aloud and free-form tasks. The recordings have been manually transcribed and assessed for pronunciation, fluency, range, accuracy, task achievement, and a holistic proficiency level. We present also an ASR and ASA benchmarking setup we have constructed using this data and include results from our baseline systems built by fine-tuning self-supervised multilingual model for the target language. In addition to benchmarking, our baseline system can be used by L2 students and teachers for online self-training and evaluation of oral proficiency.
Cite as: Kurimo, M., Getman, Y., Voskoboinik, E., Al-Ghezi, R., Kallio, H., Kuronen, M., von Zansen, A., Hilden, R., Kronholm, S., Huhta, A., Linden, K. (2023) New data, benchmark and baseline for L2 speaking assessment for low-resource languages. Proc. 9th Workshop on Speech and Language Technology in Education (SLaTE), 166-170, doi: 10.21437/SLaTE.2023-32
@inproceedings{kurimo23_slate,
author={Mikko Kurimo and Yaroslav Getman and Ekaterina Voskoboinik and Ragheb Al-Ghezi and Heini Kallio and Mikko Kuronen and Anna {von Zansen} and Raili Hilden and Sirkku Kronholm and Ari Huhta and Krister Linden},
title={{New data, benchmark and baseline for L2 speaking assessment for low-resource languages}},
year=2023,
booktitle={Proc. 9th Workshop on Speech and Language Technology in Education (SLaTE)},
pages={166--170},
doi={10.21437/SLaTE.2023-32}
}