2026-07-20T11:16:35Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18521 2024-03-06T10:32:09Z ddc:004 open_access

Automatic Speech Recognition of Non-Native Child Speech for Language Learning Applications (Short Paper) Wills, Simone Bai, Yu Tejedor-García, Cristian Cucchiarini, Catia Strik, Helmer Automatic Speech Recognition ASR Child Speech Non-Native Speech Human-computer Interaction Whisper Wav2Vec2.0 Voicebots have provided a new avenue for supporting the development of language skills, particularly within the context of second language learning. Voicebots, though, have largely been geared towards native adult speakers. We sought to assess the performance of two state-of-the-art ASR systems, Wav2Vec2.0 and Whisper AI, with a view to developing a voicebot that can support children acquiring a foreign language. We evaluated their performance on read and extemporaneous speech of native and non-native Dutch children. We also investigated the utility of using ASR technology to provide insight into the children’s pronunciation and fluency. The results show that recent, pre-trained ASR transformer-based models achieve acceptable performance from which detailed feedback on phoneme pronunciation quality can be extracted, despite the challenging nature of child and non-native speech. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Simone Wills and Yu Bai and Cristian Tejedor-García and Catia Cucchiarini and Helmer Strik 2023 Is Part Of OASIcs, Volume 113, 12th Symposium on Languages, Applications and Technologies (SLATE 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.SLATE.2023.7 urn:nbn:de:0030-drops-185218 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.SLATE.2023.7 eng https://creativecommons.org/licenses/by/4.0/legalcode