Recognizing Time Pressure and Cognitive Load on the Basis of Speech: An Experimental Study

Müller, Christian; Großmann-Hutter, Barbara; Jameson, Anthony; Rummer, Ralf; Wittig, Frank

doi:10.1007/3-540-44566-8_3

Cited by 53 publications

(41 citation statements)

References 5 publications

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“…Some researches Mueller et al, 2001;Jameson et al, 2006 showed that some speech features are related to a person's cognitive load levels, such as filled pauses and the number of sentence fragments, and tried to recognize cognitive load levels from a number of high level features by using Bayesian network (Mueller et al, 2001;Jameson et al, 2006). Word frequency and use of first-person plurals are also used to estimate cognitive load.…”

Section: Linguistic Feature Based Measuresmentioning

confidence: 99%

Robust Multimodal Cognitive Load Measurement (RMCLM)

Chen¹

2013

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This report summarizes the important research activities, study results and research accomplishments out of the RMCLM project in the past year period. The objective of this project includes research of the fundamental issues related to the use of multiple input modalities and their fusion to enable robust and automatic cognitive load measurement (CLM) in the real world. Firstly, we carried out a further literature review on physiological measures of cognitive workload to include the recent advances of physiological measures of cognitive workload. In the meantime, we examined the use of various features (e.g. spectral and approximate entropies, wavelet-based complexity measures, correlation dimension, Hurst exponent) of electroencephalogram (EEG) signals to evaluate changes in working memory load during the performance of a cognitive task with varying difficulty/load levels. Eye based CLM was also studied. Three types of eye activity were investigated: pupillary response, blink, and eye movement (fixation and saccade). We further investigated the linguistic feature based CLM in this study and analyzed novel linguistic features as potential indices of cognitive load. All together, we had carried out CLM study of three unobtrusive modalities, namely EEG, eye activity, and linguistic feature based CLM, in the past year period. List of Publications Report Documentation PageForm Approved OMB No. Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, to Washington Headquarters Services, Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to a penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. REPORT DATE APR 20132. REPORT TYPE This report summarizes the important research activities, study results and research accomplishments out of the RMCLM project in the past year period. The objective of this project includes research of the fundamental issues related to the use of multiple input modalities and their fusion to enable robust and automatic cognitive load measurement (CLM) in the real world. Firstly, we carried out a further literature review on physiological measures of cognitive workload to include the recent advances of physiological measures of cognitive workload. In the meantime, we examined the use of various features (e.g. spectral and approximate entropies, wavelet-based complexity measures, correlation dimension, Hurst exponent) of electroencephalogram (EEG) signals to evaluate changes ...

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Section: Linguistic Feature Based Measuresmentioning

confidence: 99%

Robust Multimodal Cognitive Load Measurement (RMCLM)

Chen¹

2013

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“…These BNs serve as time slices for the DBN. A BN for the interpretation of speech symptoms was learned on the basis of two experiments (see Müller, Großmann-Hutter, Jameson, Rummer, & Wittig, 2001;Kiefer, 2002), while another BN for the interpretation of features of manual input behavior was constructed on the basis of a literature study (see Lindmark, 2000). The combination of these two BNs allows READY to make inferences on the basis of multimodal imput.…”

Section: Example Application Domainmentioning

confidence: 99%

An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks

Brandherm

Jameson

2004

Int. J. Intell. Syst.

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We extend the differential approach to inference in Bayesian networks (BNs) (Darwiche, 2000) to handle specific problems that arise in the context of dynamic Bayesian networks (DBNs). We first summarize Darwiche's approach for BNs, which involves the representation of a BN in terms of a multivariate polynomial. We then show how procedures for the computation of corresponding polynomials for DBNs can be derived. These procedures permit not only an exact roll-up of old time slices but also a constant-space evaluation of DBNs. The method is applicable to both forward and backward propagation, and it does not presuppose that each time slice of the DBN has the same structure. It is compatible with approximative methods for roll-up and evaluation of DBNs. Finally, we discuss further ways of improving efficiency, referring as an example to a mobile system in which the computation is distributed over a normal workstation and a resource-limited mobile device.

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“…pitch, prosody, pauses, and disfluencies, have also been found to be changing under high levels of CL [4,[8][9][10]. Such measures allow non-intrusive analysis as they are based on speech data generated by users while they complete the task.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Load Measurement from User’s Linguistic Speech Features for Adaptive Interaction Design

Khawaja

Chen

Owen

et al. 2009

Human-Computer Interaction – INTERACT 2009

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Abstract. An adaptive interaction system, which is aware of the user's current cognitive load (CL), can change its response, presentation and flow of interaction material accordingly, to improve user's experience and performance. We present a speech content analysis approach to CL measurement, which employs users' linguistic features of speech to determine their experienced CL level. We show analyses of several linguistic features, extracted from speech of personnel working in computerized incident control rooms and involved in highly complex bushfire management tasks in Australia. We present the results of linguistic features showing significant differences between the speech from the cognitively low load and high load tasks. We also discuss how the method may be used for user interface evaluation and interaction design improvement.

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Recognizing Time Pressure and Cognitive Load on the Basis of Speech: An Experimental Study

Cited by 53 publications

References 5 publications

Robust Multimodal Cognitive Load Measurement (RMCLM)

Robust Multimodal Cognitive Load Measurement (RMCLM)

An extension of the differential approach for Bayesian network inference to dynamic Bayesian networks

Cognitive Load Measurement from User’s Linguistic Speech Features for Adaptive Interaction Design

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