1. Are Mobile In-Car Communication Systems
Feasible?
A Usability Study
Authors:
Patrick Tchankue, Janet Wesson and Dieter Vogts
SAICSIT '12, October 1–3, 2012, Centurion, Tshwane, South Africa

2. Agenda
• Introduction
• In-Car Communication Systems (ICCS)
• Speech User Interfaces (SUI)
• Design and Implementation
• User Study
• Results
• Conclusion & Future work

3. Introduction
• Driver distraction can be caused by the use of mobile
phone
• Sending text messages affects the driver the most
• Text messaging increases the crash risk by a factor of
23
• Mobile speech user interface (SUI) are one of the
proposed solutions
• But the usability of mobile SUI has not been widely
investigated
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4. In-Car Communication Systems (ICCS)
• ICCS are a subset of In-Vehicle Infotainment
Systems (IVIS):
– Sending/Receiving text messages
– Making/Answering calls
• Limited access in developing countries:
– Limited to several car models
– Involve extra costs: e.g. Ford’s SYNC: $395
• Examples:
– DriveSafe.ly, VoiceTalk, S Voice, Siri
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5. Speech User Interface (SUI)
• Speech UI or Voice UIs:
– Allows interaction with systems through speech
– Widely used in multitasking situations (e.g. Car, Kitchen)
– Guarantee eyes-free and hands-free interactions
Text-To-Speech Language generation
Dialogue manager
Database
Speech recognition Language understanding
Typical architecture for voice-activated application
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6. Proposed Design (MIMIC)
Input Module Output Module
1 Text-To-Speech Natural Language
Automatic Speech
Sensors (TTS) Generation
Recognition 9 8
GPS
Web services
Natural Language 2
3
Understanding Dialogue Module
6 Dialogue manager
7
Input pre-processing 4
and
Data fusion
Context-aware
module
Frames
Peer’s phone
5
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7. Dialogue Module
• Frame approach:
– CALL(number),
– SMS(number, content),
– REDIAL, REPEAT and CANCEL
• Prompts user (SMS)
• Grounding to handle uncertainty
• Confirmation before execution
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8. Implementation
• Speech engine:
– Google’s cloud-based speech recogniser
– RecognitionListener’s library fully hands-free
• Natural Language Understanding:
– Commands, keywords, homophones and synonyms
• Text To Speech:
– Native Android text-to-speech
– Female voice, normal pitch and rate
– Samsung Galaxy SII
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9. User Study
• Selection of participants:
– 10 participants: students from 18 to 28 year old
– 95 % of usability issues can be found (Nielsen & Landauer, 1993)
• Apparatus and Procedure:
– Lane Change Test
– Android phone
– Steering wheel and pedals
• Metrics:
– Workload
– Time-on-task in seconds
– Error on task
– Task completion
– Success rate.
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10. Task List
ID Task Description
Please call the contact Maria
T01 Send the text message
- Say “Call Maria” “I will call you when I get there” to Peter:
- Say “Text Peter”
- Answer “Yes”
T05
- Say ”One” to choose the first option
- Say “Yes” to send the selected text message
Send the text message “I am running a few minutes late” to John:
- Say “SMS John”
T02
- Say ”three” to choose the third option
Send the text message “I can’t talk text message driving” to Janet:
- Say “Yes” to send the selected right now, I am
- Say “SMS Janet”
T06
- Say “Two” to choose the call:
Redial the previous outgoing second option
T03 - Say “Redial” confirm the message
“Yes” to
- Answer “Yes”
Call a number :
Please call Diana
T07
T04 - Say “Call 074 456 1245”
- Say “Phone Diana”
- Say “Yes” to confirm the number
- Answer “Yes”
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11. Number of Errors
5.00
4.50
4.00
3.00
3.50
3.00 2.38
2.50
2.00
1.50 0.75
0.33 0.25 0.50
1.00 0.25
0.50
0.00
T01 T02 T03 T04 T05 T06 T07
Means of errors for each task (n=10)
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12. Time-on-task Results
300
250
200
150
100
50
0
T01 T02 T03 T04 T05 T06 T07
Time (seconds) spent on each task (n=10)
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13. Task Completion and Success Rate
ID Completion Success
T01 10 9
T02 10 9
T03 6 6
T04 10 10
T05 10 9
T06 10 10
T07 7 6
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14. Workload NASA TLX
5.00
4.50
4.00 3.50
3.50
3.50 3.00
3.00 2.50
2.50 2.00
2.00 1.50
1.50
1.00
0.50
0.00
Mental demand Physical demand Temporal demand Frustration Effort Good performance
Means of the user workload (n=10)
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15. User Satisfaction Results
Lot of learning needed 1.00
Cumbersome 2.00
Inconsistency 2.00
Assistance needed 2.00
Complexity 2.00
Confidence 3.00
Ease of learning 4.00
Integration 4.00
Ease of use 4.00
Willingness to use frequently 3.50
0.00 0.50 1.00 1.50 2.00 2.50 3.00 3.50 4.00 4.50 5.00
Means of user satisfaction (System Usability Scale) (n = 10)
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16. Self-reported metrics on the SUI
6
5
4
3
3
2
2
1 1
1
0
Poor recognition (confusion) Voice not recognised Clear utterance (TTS) Turn taking
Means of self-reported metrics on the SUI (n=10)
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17. Conclusion and Future work
• Mobile SUI can only be useful if there are few
usability issues and recognition rate is high.
• The SUI of MIMIC was effective in sending text
messages and making calls
• The user dictation was a source of errors
• Future work:
– Implementation of the context-aware module
– Weather, GPS and sensor information will be used to
determine possible distraction level (from 1 to 5)
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18. Thank You
Contact:
Emails: Patrick.TchankueSielinou@nmmu.ac.za
Janet.Wesson@nmmu.ac.za
Dieter.Vogts@nmmu.ac.za
Phone Numbers: 041 504 2323, 041 504 2088