CaptainA self-study mobile app for practising speaking: task completion assessment and feedback with generative AI

We introduce the CaptainA mobile app, designed to meet the needs of second language (L2) learners engaged in self- study of Finnish, with potential applicability to other languages. Our app can provide automatic speaking assessment (ASA) of task completion in picture-based tasks, along with grading ex- planations and corrective feedback. It can also automatically generate pictures for visual tasks, providing users with unlim- ited practice opportunities. The mobile app is based on our framework that combines visual natural language generation (NLG), automatic speech recognition (ASR), and prompting large language model (LLM) for low-resource language. Our goal is to promote the development of next-generation speech- based computer-assisted language learning (CALL) systems ca- pable of providing automatic scoring with feedback for learn- ers, even when minimal speech data of L2 learners is available. While the mobile app demonstration is designed for Finnish, the app can also be tested in English.

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Publication Details

Journal
Aaltodoc (Aalto University)
Published
2024-09-01
Primary Topic
Speech and dialogue systems
Type
article
Field-Weighted Citation Impact
0.00

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article

CaptainA self-study mobile app for practising speaking: task completion assessment and feedback with generative AI

Aaltodoc (Aalto University)
Speech and dialogue systems
article

CaptainA self-study mobile app for practising speaking: task completion assessment and feedback with generative AI

article en

Abstract

We introduce the CaptainA mobile app, designed to meet the needs of second language (L2) learners engaged in self- study of Finnish, with potential applicability to other languages. Our app can provide automatic speaking assessment (ASA) of task completion in picture-based tasks, along with grading ex- planations and corrective feedback. It can also automatically generate pictures for visual tasks, providing users with unlim- ited practice opportunities. The mobile app is based on our framework that combines visual natural language generation (NLG), automatic speech recognition (ASR), and prompting large language model (LLM) for low-resource language. Our goal is to promote the development of next-generation speech- based computer-assisted language learning (CALL) systems ca- pable of providing automatic scoring with feedback for learn- ers, even when minimal speech data of L2 learners is available. While the mobile app demonstration is designed for Finnish, the app can also be tested in English.

Aaltodoc (Aalto University)
University of Helsinki (FI), University of Jyväskylä (FI)
NordForsk
Quality Education
Openalex Percentile: Top 55%
Speech and dialogue systems
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