Automatic Rating of Spontaneous Speech for Low-Resource Languages
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A4 Artikkeli konferenssijulkaisussa
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en
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7
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2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings, pp. 339-345
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.Description
Funding Information: This work is part of Digitala project which is funded by the Academy of Finland (grant numbers 322619, 322625, 322965). The computational resources were provided by Aalto ScienceIT. Publisher Copyright: © 2023 IEEE.
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Al-Ghezi, R, Getman, Y, Voskoboinik, E, Singh, M & Kurimo, M 2023, Automatic Rating of Spontaneous Speech for Low-Resource Languages. in 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings. 2022 IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings, IEEE, pp. 339-345, IEEE Spoken Language Technology Workshop, Doha, Qatar, 09/01/2023. https://doi.org/10.1109/SLT54892.2023.10022381