Automated content assessment and feedback for Finnish L2 learners in a picture description speaking task

Nhan Phan, Anna von Zansen, Maria Kautonen, Ekaterina Voskoboinik, Tamás Grósz, Raili Hilden, Mikko Kurimo

Forskningsoutput: Kapitel i bok/rapport/konferenshandlingKonferensbidragVetenskapligPeer review

Sammanfattning

We propose a framework to address several unsolved challenges in second language (L2) automatic speaking assessment (ASA) and feedback. The challenges include: 1. ASA of visual task completion, 2. automated content grading and explanation of spontaneous L2 speech, 3. corrective feedback generation for L2 learners, and 4. all the above for a language that has minimal speech data of L2 learners. The proposed solution combines visual natural language generation (NLG), automatic speech recognition (ASR) and prompting a large language model (LLM) for low-resource L2 learners. We describe
the solution and the outcomes of our case study for a picture description task in Finnish. Our results indicate substantial agreement with human experts in grading, explanation and feedback. This framework has the potential for a significant impact in constructing next-generation computer-assisted language learning systems to provide automatic scoring with feedback for learners of low-resource languages.
Originalspråkengelska
Titel på värdpublikationProceedings of Interspeech 2024
Antal sidor5
FörlagISCA - International Speech Communication Association
Utgivningsdatum2024
Sidor317–321
DOI
StatusPublicerad - 2024
MoE-publikationstypA4 Artikel i en konferenspublikation
EvenemangInterspeech - Kos, Grekland
Varaktighet: 1 sep. 20245 sep. 2024
Konferensnummer: 25
https://interspeech2024.org/

Publikationsserier

NamnInterspeech
FörlagISCA
ISSN (elektroniskt)2958-1796

Vetenskapsgrenar

  • 516 Pedagogik

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