Despite increasing awareness of Alexa's potential as an educational tool, there remains a limited scope for Alexa skills to accommodate the features required for effective language learning. This paper describes an investigation into implementing 'spaced-repetition', a non-trivial feature of flashcardbased learning, through the development of an Alexa skill called 'Japanese Flashcards'. Here we show that existing Alexa development features such as skill persistence allow for the effective implementation of spaced-repetition and suggest a heuristic adaptation of the spaced-repetition model that is appropriate for use with voice assistants (VAs). We also highlight areas of the Alexa development process that limit the facilitation of language learning, namely the lack of multilingual speech recognition, and offer solutions to these current limitations. Overall, the investigation shows that Alexa can successfully facilitate simple L2-L1 flashcard-based language learning and highlights the potential for Alexa to be used as a sophisticated and effective language learning tool.
The term 'phoneme' lies at the heart of speech science and technology, and yet it is not clear that the research community fully appreciates its meaning and implications. In particular, it is suspected that many researchers use the term in a casual sense to refer to the sounds of speech, rather than as a well defined abstract concept. If true, this means that some sections of the community may be missing an opportunity to understand and exploit the implications of this important psychological phenomenon. Here we review the correct meaning of the term 'phoneme' and report the results of an investigation into its use/misuse in the accepted papers at INTERSPEECH-2018. It is confirmed that a significant proportion of the community (i) may not be aware of the critical difference between 'phonetic' and 'phonemic' levels of description, (ii) may not fully understand the significance of 'phonemic contrast', and as a consequence, (iii) consistently misuse the term 'phoneme'. These findings are discussed, and recommendations are made as to how this situation might be mitigated.
No abstract
Incremental disfluency detection provides a framework for computing communicative meaning from hesitations, repetitions and false starts commonly found in speech. One application of this area of research is in dialogue-based computer-assisted language learning (CALL), where detecting learners' production issues word-by-word can facilitate timely and pedagogically driven responses from an automated system. Existing research on disfluency detection in learner speech focuses on disfluency removal for subsequent downstream tasks, processing whole utterances non-incrementally. This paper instead explores the application of laughter as a feature for incremental disfluency detection and shows that when combined with silence, these features reduce the impact of learner errors on model precision as well as lead to an overall improvement of model performance. This work adds to the growing body of research incorporating laughter as a feature for dialogue processing tasks and provides further support for the application of multimodality in dialogue-based CALL systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.