Skip to main content

User Vocabulary Choices of the Voice Commands for Controlling In-Vehicle Navigation Systems

  • Conference paper
  • First Online:
HCI International 2020 – Late Breaking Posters (HCII 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1294))

Included in the following conference series:

Abstract

Voice control is becoming a popular technology for in-vehicle user interactions. One of the primary design issues of voice control systems is the accuracy of voice recognition. Providing a limited vocabulary set for user-system interactions is known to be a viable strategy for enhancing the system usability and user experience in voice interactions. From the human factors perspective, the development of such a vocabulary set of voice commands for system controls should be based on user intuitions rather than technical specifications. Previous researches were mostly focusing on the recognition of specifying trip destinations, less attention has been allocated on the overall interface controls of in-vehicle navigation systems.

This study aims to preliminarily explore potential patterns of the vocabulary choices of voice commands for in-vehicle navigation systems from the users’ perspective. Through a comprehensive market research, a set of 17 control functions, such as map orientation, zooming, navigation-related information, and general interface operations, of user interface interactions commonly used in modern in-vehicle navigation systems was instrumented for our experiments. Video clips showing the transitions of before-and-after scenario images for every control function were presented and prompted to experimental participants for their intuitions of the voice command vocabulary. All the intuitive voice commands collected for each control function were sorted in patterns of word cloud for further analysis. Post-experiment interviews were conducted for the subjective evaluation of the easiness of prompting voice commands and the possible reasons behind.

A total of 30 Mandarin-speaking subjects (19 males and 11 females) in Taiwan with at least 2 years of driving experience voluntarily participated in our experiment. A great discrepancy in the variety of vocabulary choices among control functions was demonstrated in our analysis. As our data indicated, for example, volume control functions (volume-up/-down/mute) are with the vocabulary choices with the most consistent results among participants, while the control functions regarding presenting detailed navigational information, such as real-time traffic situations and nearby POIs, were the poorest. Subjective preferences showed a slightly different pattern to the objective data in vocabulary choices. Recommendations to the voice interface design for in-vehicle navigation systems and future research venues are further discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Barón, A., Green, P.: Safety and usability of speech interfaces for in-vehicle tasks while driving: A brief literature review (Techical report. No. UMTRI-2006-5). University of Michigan Transportation Research Institute, Ann Arbor, MI (2006)

    Google Scholar 

  • Carter, C., Graham, R.: Experimental comparison of manual and voice controls for the operation of in-vehicle systems. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 44, pp. 3-286–3-289 (2000)

    Google Scholar 

  • Chang, C.C., Boyle, L.N., Lee, J.D., Jenness, J.: Using tactile detection response tasks to assess in-vehicle voice control interactions. Transp. Res Part F: Traffic Psychol. Behav. 51, 38–46 (2017)

    Article  Google Scholar 

  • Cooper, J.M., Ingebretsen, H., Strayer, D.L.: Mental Workload of Common Voice-Based Vehicle Interactions across Six Different Vehicle Systems. AAA Foundation for Traffic Safety, Washington, DC (2014)

    Google Scholar 

  • Engström, J., Johansson, E., Östlund, J.: Effects of visual and cognitive load in real and simulated motorway driving. Transp. Res. Part F: Traff. Psychol. Behav. 8, 97–120 (2005)

    Article  Google Scholar 

  • Garay-Vega, L., et al.: Evaluation of different speech and touch interfaces to in-vehicle music retrieval systems. Accid. Anal. Prev. 42(3), 913–920 (2010)

    Google Scholar 

  • Gellatly, A.W., Dingus, T.A.: Speech recognition and automotive applications: using speech to perform in-vehicle tasks. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 42, pp. 1247–1251. SAGE Publications (1998)

    Google Scholar 

  • Kou, X.Y., Xue, S.K., Tan, S.T.: Knowledge-guided inference for voice-enabled CAD. Comput. Aided Des. 42(6), 545–557 (2010)

    Article  Google Scholar 

  • Kun, A.L., Paek, T., Medenica, Z.: The effect of speech interface accuracy on driving performance. In: INTERSPEECH, pp. 1326–1329 (2007)

    Google Scholar 

  • Labiale, G.: In-car road information: Comparisons of auditory and visual presentations. In: Proceedings of the Human Factors Society Annual Meeting, vol. 34, No. 9, pp. 623–627. SAGE Publications, Los Angeles, October 1990

    Google Scholar 

  • Labiale, G., Ouadou, K., David, B.T.: A software system for designing and evaluating in-car information system interfaces. In: Analysis, Design and Evaluation of Man-Machine Systems 1992, pp. 257–262. Pergamon (1993)

    Google Scholar 

  • Lee, J.D., Caven, B., Haake, S., Brown, T.L.: Speech-based interaction with in-vehicle computers: the effect of speech-based e-mail on drivers’ attention to the roadway. Hum. Factors 43(4), 631–640 (2001)

    Article  Google Scholar 

  • McCallum, M., Campbell, J., Richman, J., Brown, J., Wiese, E.: Speech recognition and in-vehicle telematics devices: potential reductions in driver distraction. Int. J. Speech Technol. 7, 25–33 (2004)

    Article  Google Scholar 

  • Niezgoda, M., Tarnowski, A., Kruszewski, M., Kamiński, T.: Towards testing auditory–vocal interfaces and detecting distraction while driving: a comparison of eye-movement measures in the assessment of cognitive workload. Transp. Res. Part F: Traff. Psychol. Behav. 32, 23–34 (2015)

    Article  Google Scholar 

  • Putze, F., Schultz, T.: Cognitive dialog systems for dynamic environments: progress and challenges. In: Hansen, J., Boyraz, P., Takeda, K., Abut, H. (eds.) Digital Signal Processing for In-Vehicle Systems and Safety. Springer, New York (2012)

    Google Scholar 

  • Savchenko, A.V., Savchenko, L.V.: Towards the creation of reliable voice control system based on a fuzzy approach. Pattern Recogn. Lett. 65, 145–151 (2015)

    Article  Google Scholar 

  • Shutko, J., Mayer, K., Lansoo, E., Tijerina, L.: Driver workload effects of cell phone music player, and text messaging tasks with the Ford SYNC voice interface versus hand-held visual-manual interfaces (Paper No. 2009-01-0786). Society of Automotive Engineering (2009)

    Google Scholar 

  • Simmons, S.M., Caird, J.K., Steel, P.: A meta-analysis of in-vehicle and nomadic voice-recognition system interaction and driving performance. Accid. Anal. Prev. 106, 31–43 (2017)

    Article  Google Scholar 

  • Strayer, D.L., Cooper, J.M., Turrill, J., Coleman, J., Medeiros-Ward, N., Biondi, F.: Measuring cognitive distraction in the automobile (2013a)

    Google Scholar 

  • Strayer, D.L., Cooper, J.M., Turrill, J., Coleman, J., Medeiros-Ward, N., Biondi, F.: Measuring Cognitive Distraction in the Automobile. AAA Foundation for Traffic Safety, Washington, DC (2013b)

    Google Scholar 

  • Tijerina, L.: Driver distraction and road safety. In: Smiley, A. (ed.) Human Factors in Traffic Safety, 3rd edn., pp. 219–276. Lawyers and Judges Publishing, Tuscon, AZ (2016)

    Google Scholar 

  • Tijerina, L., Parmer, E., Goodman, M.J.: Driver workload assessment of route guidance system destination entry while driving: a test track study. In: Proceedings of the 5th World Congress on Intelligent Transport Systems [CD-ROM], Washington, DC: ITSA (1998)

    Google Scholar 

  • Wang, G., Sim, K.C.: Context dependent acoustic keyword spotting using deep neural network. In: 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1–10. IEEE (2013)

    Google Scholar 

  • Wu, J., Chang, C.-C., Boyle, L.N., Jenness, J.: Impact of in-vehicle voice control systems on driver distraction insights from contextual interviews. Proc. Hum. Factors Ergon. Soc. Ann. Meeting 59, 1583–1587 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to An-Che Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chen, AC., Li, MS., Lin, CY., Li, MC. (2020). User Vocabulary Choices of the Voice Commands for Controlling In-Vehicle Navigation Systems. In: Stephanidis, C., Antona, M., Ntoa, S. (eds) HCI International 2020 – Late Breaking Posters. HCII 2020. Communications in Computer and Information Science, vol 1294. Springer, Cham. https://doi.org/10.1007/978-3-030-60703-6_70

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-60703-6_70

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-60702-9

  • Online ISBN: 978-3-030-60703-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics