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

Abstract

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.