research-article

Do speech features for detecting cognitive load depend on specific languages?

Authors:

Ningjiu TangAuthors Info & Claims

ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal Interaction

Pages 76 - 83

https://doi.org/10.1145/2993148.2993149

Published: 31 October 2016 Publication History

Abstract

Speech-based cognitive load modeling recently proposed in English have enabled objective, quantitative and unobtrusive evaluation of cognitive load without extra equipment. However, no evidence indicates that these techniques could be applied to speech data in other languages without modification. In this study, a modified Stroop Test and a Reading Span Task were conducted to collect speech data in English and Chinese respectively, from which twenty non-linguistic features were extracted to investigate whether they were language dependent. Some discriminating speech features were observed language dependent, which serves as an evidence that there is a necessity to adapt speech-based cognitive load detection techniques to diverse language contexts for a higher performance.

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Cited By

Schneegass CWilson MShaban JNiess JChiossi FMitrevska TWoźniak P(2024)Broadening the mind: how emerging neurotechnology is reshaping HCI and interactive system designi-com10.1515/icom-2024-000723:2(165-177)Online publication date: 23-May-2024
https://doi.org/10.1515/icom-2024-0007
Alt FHassib MDistler V(2023)Human-centered Behavioral and Physiological SecurityProceedings of the 2023 New Security Paradigms Workshop10.1145/3633500.3633504(48-61)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1145/3633500.3633504
Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Show More Cited By

Index Terms

Do speech features for detecting cognitive load depend on specific languages?
1. Human-centered computing
  1. Human computer interaction (HCI)
    1. Empirical studies in HCI

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cover image ACM Conferences

ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal Interaction

October 2016

605 pages

ISBN:9781450345569

DOI:10.1145/2993148

General Chairs:
Yukiko I. Nakano
Seikei University, Japan
,
Elisabeth André
Augsburg University, Germany
,
Toyoaki Nishida
Kyoto University, Japan
,
Program Chairs:
Louis-Philippe Morency
Carnegie Mellon University, USA
,
Carlos Busso
University of Texas at Dallas, USA
,
Catherine Pelachaud
ISIR, France / University of Paris6, France

Copyright © 2016 ACM.

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Publication History

Published: 31 October 2016

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Science and Technology Supporting Program, Sichuan Province

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ICMI '16

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SIGCHI

ICMI '16: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

November 12 - 16, 2016

Tokyo, Japan

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Overall Acceptance Rate 453 of 1,080 submissions, 42%

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Cited By

Schneegass CWilson MShaban JNiess JChiossi FMitrevska TWoźniak P(2024)Broadening the mind: how emerging neurotechnology is reshaping HCI and interactive system designi-com10.1515/icom-2024-000723:2(165-177)Online publication date: 23-May-2024
https://doi.org/10.1515/icom-2024-0007
Alt FHassib MDistler V(2023)Human-centered Behavioral and Physiological SecurityProceedings of the 2023 New Security Paradigms Workshop10.1145/3633500.3633504(48-61)Online publication date: 18-Sep-2023
https://dl.acm.org/doi/10.1145/3633500.3633504
Kosch TKarolus JZagermann JReiterer HSchmidt AWoźniak P(2023)A Survey on Measuring Cognitive Workload in Human-Computer InteractionACM Computing Surveys10.1145/358227255:13s(1-39)Online publication date: 13-Jul-2023
https://dl.acm.org/doi/10.1145/3582272
Pyfrom MLister JAnand S(2023)Influence of Cognitive Load on Voice Production: A Scoping ReviewJournal of Voice10.1016/j.jvoice.2023.08.024Online publication date: Sep-2023
https://doi.org/10.1016/j.jvoice.2023.08.024
Hecker PKappattanavar ASchmitt MMoontaha SWagner JEyben FSchuller BArnrich B(2022)Quantifying Cognitive Load from Voice using Transformer-Based Models and a Cross-Dataset Evaluation2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)10.1109/ICMLA55696.2022.00055(337-344)Online publication date: Dec-2022
https://doi.org/10.1109/ICMLA55696.2022.00055

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