Abstract
Quality of synthetic speech and auditory working memory performance: neuroergonomic perspectives from fNIRS
Frontiers in human neuroscience, v 12
2018
Abstract
The way in which we effortlessly understand speech makes it relatively easy to overlook the complexity of the neural mechanisms which support its operation. This perceived ease of speech comprehension has inspired researchers and interface designers seek to incorporate additional auditory channels of information to consumer and industrial devices, especially in circumstances where the user of the device is otherwise engaged in manual or visual tasks such as handsfree communication while driving. However, previous research has demonstrated that there are unconscious cognitive costs associated with simple speech comprehension which can be exacerbated by task performance(Treffner & Barrett, 2004) and audio quality(Delogu, Conte, & Sementina, 1998; Francis & Nusbaum, 2009). In a previous exploratory work, we have observed performance deficits and neural activation differences due to low-quality synthetic speech during speech comprehension tasks(Curtin & Ayaz, 2017), and here we extend this exploration by examining how the quality of synthetic speech affects auditory working memory (AWM) performance and neural load using functional Near Infrared Spectroscopy (fNIRS), a continuous and noninvasive neuroimaging technique capable of investigating brain dynamics under naturalistic and applied settings (Ayaz et al., 2013).
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Details
- Title
- Quality of synthetic speech and auditory working memory performance: neuroergonomic perspectives from fNIRS
- Creators
- Adrian Curtin - Drexel University, School of Biomedical Engineering, Science, and Health SystemsHasan Ayaz - Drexel University, School of Biomedical Engineering, Science, and Health Systems
- Publication Details
- Frontiers in human neuroscience, v 12
- Conference
- The 2nd International Neuroergonomics Conference (Philadelphia, Pennsylvania, United States, 27 Jun 2018–29 Jun 2018)
- Resource Type
- Abstract
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Other Identifier
- 991019186697904721