Combining oculo-motor indices to measure cognitive load of synthetic speech in noisy listening conditions
Dubiel, Mateusz and Nakayama, Minoru and Wang, Xin; Spencer, Stephen N., ed. (2021) Combining oculo-motor indices to measure cognitive load of synthetic speech in noisy listening conditions. In: ETRA '21 Short Papers : ACM Symposium on Eye Tracking Research and Applications. Eye Tracking Research and Applications Symposium (ETRA) . ACM, DEU. ISBN 9781450383455 (https://doi.org/10.1145/3448018.3458013)
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Abstract
Gaze-based assistive technologies (ATs) that feature speech have the potential to improve the life of people with communication disorders. However, due to a limited understanding of how different speech types affect the cognitive load of users, an evaluation of ATs remains a challenge. Expanding on previous work, we combined temporal changes in pupil size and ocular movements (saccades and fixation differentials) to evaluate cognitive workload of two types of speech (natural and synthetic) mixed with noise, through a listening test. While observed pupil sizes were significantly larger at lower signal-to-noise levels, as participants listened and memorised speech stimuli; saccadic eye-movements were significantly more frequent for synthetic speech. In the synthetic condition, there was a strong negative correlation between pupil dilation and fixation differentials, indicating a higher strain on participants’ cognitive resources. These results suggest that combining oculo-motor indices can aid our understanding of the cognitive implications of different speech types.
ORCID iDs
Dubiel, Mateusz ORCID: https://orcid.org/0000-0001-8250-3370, Nakayama, Minoru and Wang, Xin; Spencer, Stephen N.-
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Item type: Book Section ID code: 76047 Dates: DateEvent25 May 2021Published18 March 2021AcceptedSubjects: Science > Mathematics > Electronic computers. Computer science Department: Faculty of Science > Computer and Information Sciences Depositing user: Pure Administrator Date deposited: 08 Apr 2021 11:18 Last modified: 11 Nov 2024 15:25 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/76047