Modeling Driving Performance Using In-Vehicle Speech Data From a Naturalistic Driving Study. (September 2016)

• Record Type: Journal Article
• Authors: Kuo, Jonny; Charlton, Judith L.; Koppel, Sjaan; Rudin-Brown, Christina M.; Cross, Suzanne
• Is Part Of: Human factors. Volume 58:Number 6(2016)
• Page Start: 833
• Page End: 845
• Publication Date: 2016-09
• Subjects: naturalistic driving study -- driver distraction -- speech activity detection -- speaker diarization -- child passengers; Human engineering -- Periodicals; 620.82
• DOI: 10.1177/0018720816650565
• Languages: English
• ISSNs: 0018-7208

Abstract

Objective: We aimed to (a) describe the development and application of an automated approach for processing in-vehicle speech data from a naturalistic driving study (NDS), (b) examine the influence of child passenger presence on driving performance, and (c) model this relationship using in-vehicle speech data. Background: Parent drivers frequently engage in child-related secondary behaviors, but the impact on driving performance is unknown. Applying automated speech-processing techniques to NDS audio data would facilitate the analysis of in-vehicle driver–child interactions and their influence on driving performance. Method: Speech activity detection and speaker diarization algorithms were applied to audio data from a Melbourne-based NDS involving 42 families. Multilevel models were developed to evaluate the effect of speech activity and the presence of child passengers on driving performance. Results: Speech activity was significantly associated with velocity and steering angle variability. Child passenger presence alone was not associated with changes in driving performance. However, speech activity in the presence of two child passengers was associated with the most variability in driving performance. Conclusion: The effects of in-vehicle speech on driving performance in the presence of child passengers appear to be heterogeneous, and multiple factors may need to be considered in evaluating their impact. This goal can potentially be achieved within large-scale NDS through the automated processing of observational data, including speech. Application: Speech-processing algorithms enable new perspectives on driving performance to be gained from existing NDS data, and variables that were once labor-intensive to process can be readily utilized in future research.