[Lee 2013]= Viewgraphs

Report
TMSG
SocioPhone: Everyday Face-to-Face
Interaction Monitoring Platform Using
Multi-Phone Sensor Fusion
實驗室: 先進網路技術與服務實驗室
報告者: 黃福銘 (Angus F.M. Huang)
2013.09.04
Publication
• MobiSys 2013
• Session 8: Behavior and Activity Recognition
• NuActiv: Recognizing Unseen New Activities Using Semantic AttributeBased Learning
• SocioPhone: Everyday Face-To-Face Interaction Monitoring Platform
Using Multi-Phone Sensor Fusion
• MoodScope: Building a Mood Sensor from Smartphone Usage Patterns
• Auditeur: A Mobile-Cloud Service Platform for Acoustic Event Detection
on Smartphones
Angus F.M. Huang
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Authors
• Youngki Lee, Chulhong Min, Chanyou Hwang, Jaeung Lee, Inseok
Hwang,Younghyun Ju, Chungkuk Yoo, Miri Moon, Uichin Lee, Junehwa
Song
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School of Information Systems, Singapore Management University
Computer Science Department, KAIST,
Web Science and Technology Division, KAIST
Center for Mobile Software Platform, KAIST
Knowledge Service Engineering Department, KAIST
– http://www.kaist.edu/edu.html
– (韓國科學技術院)
Angus F.M. Huang
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• Face-to-face interaction monitoring
– Interaction-aware applications
– Group conversations
– Useful meta-linguistic contexts of conversation
– Online turn monitoring
– SocioTherapist, SocioDigest, Tug-of-War
Abstract
Angus F.M. Huang
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Outline
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INTRODUCTION
SOCIOPHONE API And APPLICATIONS
IN-SITU TURN MONITORING
PLATFORM IMPLEMENTATION
EXPERIMENTS
CONCLUSION
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Introduction
• Face-to-face social interaction
• Interaction-aware applications
– Helps a user remember the name of the person
– Developers do not know which contexts to leverage during daily
conversations
• Communicative cues
– Verbal cues
• Spoken words and sentences
– Aural cues
• Tones, pitch
– Visual cues
• Gesture, eye contact
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Introduction
• Conversational turns
– The basic unit of conversation
– A turn is a continuous speech segment where a
person starts and ends her speech
• Basic aspects of a conversation
– Length, speed, participant,…
• High-level social inference
– Role, problem,…
• It will improve collaborative decision
psychological care, content analysis,…
Angus F.M. Huang
making,
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Challenges
• Mobile environment challenges
– Unconstrained acoustic situations
– Real-time monitoring
– Battery limitations
• Short-lasting turns (1-2 seconds)
• Long speech segments (3-8 seconds)
• Dynamic ambient noises
• Significant power consuming for high-rate sound
sensing and heavy computation
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SocioPhone API and Applications
• Meta-Linguistic Interaction Monitoring
• SocioPhone API
• Example Applications on SocioPhone
• Online conversation monitoring platform
– Meta-linguistic conversational contexts
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Online turn segmentation and metalinguistic conversation monitoring
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Meta-Linguistic Interaction Monitoring
• Online turn segmentation
– Execute online turn segmentation using smartphones
– Turn : (speaking person, start time, end time)
• Meta-linguistic conversation monitoring
– Track non-verbal elements during conversations
– Turn features
• Individual participant
– Speaking length, number of turns, duration statistics
• Relations among participants
– Turn taking orders, pair-wise turn-taking frequencies
• Whole interaction session
– Duration of speaking and non-speaking turns
– Prosodic features
• Pitch, energy, loudness, rhythm, formant, bandwidth, spectrum intensity
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SocioPhone API:
Monitoring sessions and turns
• registerSessionStartListener()
– start/end of a conversation
– join/leave of a participant
– Session table
• registerTurnChangeListener()
– provides the Turn information continuously upon each
turn-taking event
– Turn table
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SocioPhone API:
Monitoring meta-linguistic interactions
• enableProsodicFeatures()
– retrieve rich prosodic features associated with each turn
• getSparsity()
– returns how far the speaking turns are separated by nonspeaking turns
• registerDominanceListener()
– encapsulates complex social inference to find someone
with dominance over the conversation
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SocioPhone API:
Querying interaction history
• getOnGoingSessionHistory()
– query the ongoing session
• getPastInteractionHistory()
– query completed sessions
• Example queries
– “How many turns has John taken within last 10 minutes”
– “Which three friends has John spoken to the most this week?”
– a conventional SQL interface
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SocioPhone Application:
SocioTherapist
• Speech therapy sessions for autistic children
– Therapist often employs a stimulus
– Upon a successful response
– Reinforced with small rewards
• SocioTherapist App
– Mimic stimuli and reinforcements
– A callback is triggered for every turn-taking event
– Looks for initiations, long-lasting turns, and rapid responses
• Well-known robotic characters for children gradually
upgraded upon desirable turn-takings
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SocioPhone Application:
SocioDigest
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(a) Cumulative conversation time within the user’s social circle
(b) Relative per-person talking times in a session
(c) Relative number of turns exchanged in a session
Angus F.M. Huang
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SocioPhone Application:
Tug-of-War
• In group meetings or brainstorming
– the level of participation of each individual may vary greatly
• Balance participations from all individuals
– yield better outcomes in brainstorming
• Tug-of-War app
– monitor turn-takings of participants
– provides in-situ graphical feedback of how long each has talked so far
• The lines of kernel code of our prototype is only 75
– the effectiveness of SocioPhone to facilitate the development of
interaction-aware applications
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Challenges and Limitations
• Challenges in Daily Conversation Monitoring
– Interaction patterns
– Environmental characteristics
• Limitations of Existing Techniques
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Slow, inaccurate speaking turn detection
Vulnerability to real-life acoustic environments
High energy consumption
Limitation of existing collaborative sensing approaches
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In-situ Turn Monitoring
• Speaker recognition
– each phone measures a speaker’s voice signal strength
• Volume-peak-based algorithm
– to select a phone that has the strongest signal strength
• Limitations
– location and placement of phones are not controllable
– some of the phones may not be available
– peak detection is susceptible to background noise
• Volume topography-based method
– each speaker have a unique volume signature over multiple phones
– limit complex signal processing only in the learning phase
– enable to work even when some phones may not be available
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Illustration of online turn monitoring
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Volume Topography-based Algorithm
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Training data collection
Feature vector transformation
Topography generation
Classifier training and classification
Turn recognition
Mapping audio signatures (cluster-IDs) to group
members (member-IDs)
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Training data collection
• each phone samples the incoming sound at the rate
of 8 kHz
• audio stream is segmented into 300 ms-frames
• a given time t, each phone i calculates p(t,i)
– the power of the frame from phone i at time t
• the average of the square of the audio signals
• feature vector, P(t) = (p(t,1), p(t,2),…,p(t,np))
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Feature vector transformation
• define the feature vector so that it has discrimination power
• P(t)
– (a), performs poorly in discriminating non-speech turns (or silent turns)
• P'(t) = P(t) / E(t)
– E(t) is an average of a vector P(t)
– (b), discrimination is weak when the number of phones is less than the
group size
– (c), shows P'(t) with one fewer phone
• P''(t) = {D(t,1) × p(t,1) / E(t), …, D(t,np) × p(t,np) / E(t)}
– pref is the standard reference sound pressure level, 20μPa
– discriminate better even with fewer phones
– (d) and (e), show P''(t) using three and two phones
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Distribution of feature vectors
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Learning and Recognition
• Topography generation
– From the training dataset, we build a set of audio-signal signatures
– volume topographies
• Classifier training and classification
– multi-class SVM classifier
– After training has completed, SocioPhone segments turns online by
simply mapping incoming frames into cluster-IDs using this classifier
• Turn recognition
– A turn is detected if two consecutive frames belong to different
clusters
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Platform Implementation
• Monitoring Planner
• Source Selector first figures out how many phones participate
– checks if the phone has sufficient battery power
– sound signals are clear enough for discriminative volume topography
• Execution Planner
– performs turn monitoring with the volume-topography-based method
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Platform Implementation
• Meta-linguistic Information Processor
– Turn Detector computes turns
– Feature Extractor processes prosodic features
– Pattern Analyzer infers a number of meaningful social contexts by
combining turn information and prosodic features
• dominance and leadership in a conversation group, conversation
asymmetry, interactivity, and sparseness
– heuristic metrics
• Level of interactivity: # of speaking turns per minute
• Level of sparseness: # of non-speaking turns over three seconds
per minute
• Level of skewness: standard deviation of # of speaking turns for all
participants
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Platform Implementation
• Interaction History Manager
– supports SQL queries from applications
– SocioPhone holds the turn information for the on-going session in the
memory
– use SQLite database in Android
• Network Interface
– use Bluetooth for peer discovery and communication
• 138 mW (exchange messages every second)
– if Wi-Fi Direct
• 413 mW
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SocioPhone
System
Architecture
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Experiments
• Experimental Setup
– Scenarios and parameters
– Each conversation is 15 minutes of unscripted, free talking
– Galaxy Nexus phones
• Comparisons
– SinglePipe
– CombinePipe
• DarwinPhones, final inference
by combining GMM likelihoods
– SharePipe
• CoMon, among multiple
phones, only a single phone
runs SinglePipe and shares
the results with other phones
to save energy
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Experiments
• Evaluation metrics
– Turn-monitoring accuracy
• Accuracy = {D(TP) + D(TN)} / total time
• Precision = D(TP) / {D(TP) + D(FP)}
• Recall = D(TP) / {D(TP) + D(FN)}
– Resource efficiency in terms of energy and CPU
• Ground-truth annotation
– throat microphone records
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Turn-Monitoring Accuracy
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Angus F.M. Huang
SocioPhone captures the overall turntaking pattern well
CombinePipe also recognizes
speakers well in long-speaking turns,
but often misses short, interactive
turns.
45% of turns are less than four
seconds
More than80% of speeches are less
than 10 seconds
the topic or type of conversation could
change the distribution, but the
general trend would remain stable
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Effect of Number of Phones
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SocioPhone outperforms the others
regardless of the group size by 1219%
Even with 5 interactants, it shows the
accuracy of 83%
– while the accuracies of other
techniques are below 70%
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SocioPhone outperforms
CombinePipe except when only two
phones are available
– When the number of available phones
is much smaller than the group size,
our method performs worse than
CombinePipe
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Angus F.M. Huang
The volume topography-based
method works well even if a small
portion of phones is unavailable
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Effect of Phone Placement
• SocioPhone shows around 75%
of accuracy even with three
phones placed in a pocket and
two phones on a table
– similar to the accuracy of
CombinePipe with all five phones on
the table
• An F1 score is the harmonic
mean of precision and recall
– the F1 score of each interactants
depends on which phone is placed in
a pocket
• An imbalance of recoding volume
makes inference more difficult for
users with relatively close
positions such as A and C when
C’s phone is in C’s pocket.
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Effect of Places
Precision and recall in a natural situation
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Cost of Turn Monitoring
• A head takes charge of the coordination and final inference
• A member transmits the required information to the head
• A 1750 mAh battery would last about 23 hours with SocioPhone and 1214 hours with others
• Interestingly, CombinePipe’s member consumes 35.3 mW more than the
head, since Bluetooth consumes more power for transmission than for
reception
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Cost Breakdown of Turn Monitoring
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Conclusion
• SocioPhone
– a mobile interaction-monitoring platform
• A set of APIs
– to monitor turn and turn-derived meta-linguistic contexts
• Highlyefficient online turn-monitoring techniques
– volume topography
• SocioPhone applications
– SocioTherapist, SocioDigest, and Tug-of-War
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Angus Comments
• Extend PLASH’s daily recording to personal life
review
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Angus Comments
• From school-life trajectory distribution to School-dynamics
profiling
– School-life dynamics monitoring
– Interest-based friends exploration
– Learning-partners matchmaking
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