Developing an AI-assisted Low-resource Spoken Language Learning App for Children

• Published in: IEEE access Vol. 11; p. 1
• Main Authors: Getman, Yaroslav, Phan, Nhan, Al-Ghezi, Ragheb, Voskoboinik, Ekaterina, Singh, Mittul, Grosz, Tamas, Kurimo, Mikko, Salvi, Giampiero, Svendsen, Torbjorn, Strombergsson, Sofia, Smolander, Anna, Ylinen, Sari
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applications programs; Artificial intelligence; ASR; Children; children's speech; Computer & video games; Computer aided diagnosis; Hidden Markov models; L2 speech; Learning; Mobile computing; Pediatrics; Quality assessment; Recording; speech rating; Speech recognition; Speech sounds; SSD; Task analysis; Toy manufacturing industry; Training; Vocabulary; wav2vec2
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3304274

Computer-assisted Language Learning (CALL) is a rapidly developing area accelerated by advancements in the field of AI. A well-designed and reliable CALL system allows students to practice language skills, like pronunciation, any time outside of the classroom. Furthermore, gamification via mobile applications has shown encouraging results on learning outcomes and motivates young users to practice more and perceive language learning as a positive experience. In this work, we adapt the latest speech recognition technology to be a part of an online pronunciation training system for small children. As part of our gamified mobile application, our models will assess the pronunciation quality of young Swedish children diagnosed with Speech Sound Disorder, and participating in speech therapy. Additionally, the models provide feedback to young non-native children learning to pronounce Swedish and Finnish words. Our experiments revealed that these new models fit into an online game as they function as speech recognizers and pronunciation evaluators simultaneously. To make our systems more trustworthy and explainable, we investigated whether the combination of modern input attribution algorithms and time-aligned transcripts can explain the decisions made by the models, give us insights into how the models work and provide a tool to develop more reliable solutions.