Improving Adaptive Learning Models Using Prosodic Speech Features

Thomas Wilschut*, Florian Sense, Odette Scharenborg, Hedderik van Rijn

*Corresponding author for this work

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Cognitive models of memory retrieval aim to describe human learning and forgetting over time. Such models have been successfully applied in digital systems that aid in memorizing information by adapting to the needs of individual learners. The memory models used in these systems typically measure the accuracy and latency of typed retrieval attempts. However, recent advances in speech technology have led to the development of learning systems that allow for spoken inputs. Here, we explore the possibility of improving a cognitive model of memory retrieval by using information present in speech signals during spoken retrieval attempts. We asked 44 participants to study vocabulary items by spoken rehearsal, and automatically extracted high-level prosodic speech features—patterns of stress and intonation—such as pitch dynamics, speaking speed and intensity from over 7,000 utterances. We demonstrate that some prosodic speech features are associated with accuracy and response latency for retrieval attempts, and that speech feature informed memory models make better predictions of future performance relative to models that only use accuracy and response latency. Our results have theoretical relevance, as they show how memory strength is reflected in a specific speech signature. They also have important practical implications as they contribute to the development of memory models for spoken retrieval that have numerous real-world applications.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education - 24th International Conference, AIED 2023, Proceedings
EditorsNing Wang, Genaro Rebolledo-Mendez, Noboru Matsuda, Olga C. Santos, Vania Dimitrova
PublisherSpringer
Pages255-266
Number of pages12
ISBN (Print)9783031362712
DOIs
Publication statusPublished - 2023
Event24th International Conference on Artificial Intelligence in Education, AIED 2023 - Tokyo, Japan
Duration: 3 Jul 20237 Jul 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13916 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Artificial Intelligence in Education, AIED 2023
Country/TerritoryJapan
CityTokyo
Period3/07/237/07/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Adaptive Learning
  • Automatic Speech Recognition
  • Cognitive Modeling
  • Intensity
  • Machine learning
  • Pitch
  • Speaking Speed
  • Speech prosody

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