Speaking to remember: Model-based adaptive vocabulary learning using automatic speech recognition

Thomas Wilschut*, Florian Sense, Hedderik van Rijn

*Corresponding author for this work

    Research output: Contribution to journalArticleAcademicpeer-review

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    Memorizing vocabulary is a crucial aspect of learning a new language. While personalized learning- or intelligent tutoring systems can assist learners in memorizing vocabulary, the majority of such systems are limited to typing-based learning and do not allow for speech practice. Here, we aim to compare the efficiency of typing- and speech based vocabulary learning. Furthermore, we explore the possibilities of improving such speech-based learning using an adaptive algorithm based on a cognitive model of memory retrieval. We combined a response time-based algorithm for adaptive item scheduling that was originally developed for typing-based learning with automatic speech recognition technology and tested the system with 50 participants. We show that typing- and speech-based learning result in similar learning outcomes and that using a model-based, adaptive scheduling algorithm improves recall performance relative to traditional learning in both modalities, both immediately after learning and on follow-up tests. These results can inform the development of vocabulary learning applications that–unlike traditional systems–allow for speech-based input.

    Original languageEnglish
    Article number101578
    JournalComputer Speech and Language
    Early online date31-Oct-2023
    Publication statusPublished - Mar-2024


    • ACT-R
    • Adaptive learning
    • Automatic speech recognition (ASR)
    • Memory
    • Response Times (RT)
    • Speech
    • Typing
    • Vocabulary

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