ABSTRACT
A large number of people rate public speaking as their top fear. What if these individuals were given an intelligent interface that provides live feedback on their speaking skills? In this paper, we present Rhema, an intelligent user interface for Google Glass to help people with public speaking. The interface automatically detects the speaker's volume and speaking rate in real time and provides feedback during the actual delivery of speech. While designing the interface, we experimented with two different strategies of information delivery: 1) Continuous streams of information, and 2) Sparse delivery of recommendation. We evaluated our interface with 30 native English speakers. Each participant presented three speeches (avg. duration 3 minutes) with 2 different feedback strategies (continuous, sparse) and a baseline (no feeback) in a random order. The participants were significantly more pleased (p < 0.05) with their speech while using the sparse feedback strategy over the continuous one and no feedback.
- Batrinca, L., Stratou, G., Shapiro, A., Morency, L.-P., and Scherer, S. Cicero - towards a multimodal virtual audience platform for public speaking training. Intelligent Virtual Agents, Springer (2013), 116--128.Google Scholar
- Biocca, F., Owen, C., Tang, A., and Bohil, C. Attention Issues in Spatial Information Systems: Directing Mobile Users' Visual Attention Using Augmented Reality. Journal of Management Information Systems 23, 2007, 163--184. Google ScholarDigital Library
- Boersma, P. Accurate short-term analysis of the fundamental frequency and the harmonics-to-noise ratio of a sampled sound. IFA Proceedings 17, (1993), 97-- 110.Google Scholar
- Boersma, Paul; Weenink, D. Praat: Doing Phonetics by Computer. Ear and Hearing 32, 2011, 266.Google ScholarCross Ref
- Chollet, M. et al. An interactive virtual audience platform for public speaking training. Autonomous agents and multi-agent systems, (2014), 1657--1658. Google ScholarDigital Library
- Cohen, J. A coefficient of agreement of nominal scales. Educational and Psychological Measurement 20, (1960), 37--46.Google ScholarCross Ref
- Dunn, O. J. Multiple Comparisons Among Means. Journal of the American Statistical Association 56, (1961), 52--64.Google ScholarCross Ref
- Finlay, D. Motion perception in the peripheral visual field. Perception 11, 1982, 457--462.Google ScholarCross Ref
- Friedman, M. A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11, 1 (1940), 86--92.Google ScholarCross Ref
- Ha, K., Chen, Z., Hu, W., Richter, W., Pillai, P., and Satyanarayanan, M. Towards wearable cognitive assistance. MobiSys '14, (2014), 68--81. Google ScholarDigital Library
- Hoogterp, B. Your Perfect Presentation: Speak in Front of Any Audience Anytime Anywhere and Never Be Nervous Again. McGraw Hill Professional, 2014.Google Scholar
- Hoque, M., Courgeon, M., and Martin, J. Mach: My automated conversation coach. Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing, (2013), 697--706. Google ScholarDigital Library
- Koppensteiner, M. and Grammer, K. Motion patterns in political speech and their influence on personality ratings. Journal of Research in Personality 44, (2010), 374--379.Google ScholarCross Ref
- Krippendorff, K. Content Analysis: An Introduction to Its Methodology. 2004.Google Scholar
- McAtamney, G. and Parker, C. An examination of the effects of a wearable display on informal face-to-face communication. Proceedings of ACM CHI 2006 Conference on Human Factors in Computing Systems, (2006), 45--54. Google ScholarDigital Library
- McCrickard, D. S., Catrambone, R., Chewar, C. M., and Stasko, J. T. Establishing tradeoffs that leverage attention for utility: Empirically evaluating information display in notification systems. International Journal of Human Computer Studies 58, (2003), 547--582. Google ScholarDigital Library
- North, M. M., North, S. M., Coble, J. R., et al. Virtual reality therapy: an effective treatment for the fear of public speaking. The International Journal of Virtual Reality 3 (1998), 1 3, (1998).Google ScholarCross Ref
- Ofek, E., Iqbal, S. T., and Strauss, K. Reducing disruption from subtle information delivery during a conversation: mode and bandwidth investigation. Proceedings of CHI 2013, (2013), 3111--3120. Google ScholarDigital Library
- Pashler, H. Dual-task interference in simple tasks: data and theory. Psychological bulletin 116, (1994).Google Scholar
- Shiffrin, R. M. and Gardner, G. T. Visual processing capacity and attentional control. Journal of experimental psychology 93, (1972), 72--82.Google Scholar
- Strangert, E. and Gustafson, J. What makes a good speaker? subject ratings, acoustic measurements and perceptual evaluations. INTERSPEECH, (2008), 1688--1691.Google Scholar
- Strayer, D. L., Drews, F. A., and Crouch, D. J. Fatal distraction? A comparison of the cell-phone driver and the drunk driver. Human Factors: The Journal of the Human Factors and Ergonomics Society 48, (2006), 381--391.Google ScholarCross Ref
- Teeters, A., Kaliouby, R. El, and Picard, R. Self-Cam: feedback from what would be your social partner. ACM SIGGRAPH 2006 Research posters, (2006). Google ScholarDigital Library
- Wallechinsky, D. The book of lists. Canongate Books, 2009.Google Scholar
- Wilcoxon, F. Individual comparisons of grouped data by ranking methods. Journal of economic entomology 39, (1946), 269.Google Scholar
- Zhang, Z. Microsoft kinect sensor and its effect. MultiMedia, IEEE 19, 2 (2012), 4--10. Google ScholarDigital Library
Index Terms
- Rhema: A Real-Time In-Situ Intelligent Interface to Help People with Public Speaking
Recommendations
Native vs. non-native language fluency implications on multimodal interaction for interpersonal skills training
ICMI '16: Proceedings of the 18th ACM International Conference on Multimodal InteractionNew technological developments in the field of multimodal interaction show great promise for the improvement and assessment of public speaking skills. However, it is unclear how the experience of non-native speakers interacting with such technologies ...
Designing for Speech Practice Systems: How Do User-Controlled Voice Manipulation and Model Speakers Impact Self-Perceptions of Voice?
CHI '22: Proceedings of the 2022 CHI Conference on Human Factors in Computing SystemsCan you speak the way you desire without feeling the pressure to conform to standards of speaking? In this study, we investigated the impact of user-controlled voice manipulation and listening to recordings of model speakers on self-perceptions of voice ...
Acoustic characteristics of public speaking: Anxiety and practice effects
This study describes the relationship between acoustic characteristics, self-ratings, and listener-ratings of public speaking. The specific purpose of this study was to examine the effects of anxiety and practice on speech and voice during public ...
Comments