- 의류 쇼핑몰에서 ‘의류'전시를 포기한 사례 분석 및 통찰
- Here-and-now & answer engine으로서의 콘텐츠 서비스
- 인공지능이 탑재된 챗봇(Chatbot)의 콘텐츠 서비스에서 추구해야 할 UX전략
- 인간의 목소리(Voice) 자체가 콘텐츠 서비스가 되는 시대에서의 UX전략
- 인간의 후각을 자연스럽고 조용히(natural & silent) 자극하는 콘텐츠 서비스
- 인간의 감성 상태에 따라 자동으로 변하는 공간 내 콘텐츠 서비스
- Second hand와 공진하는 콘텐츠 서비스
- VR과 결합한 콘텐츠 서비스
15. “Use” Users For UX
“Buy” Customers For CX or ServiceDesign For Customer eXperience or Service experience
Design For User eXperience or (Interactive) product experience
16. “Use” Users For UX
“Buy” Customers For CX or Service
“Talk” Player For PX(Playful eXperience)(Saying/Typing and Respond)
Design For Customer eXperience or Service experience
Design For User eXperience or (Interactive) product experience
Design For Player’s Playful eXperience
(embodied (interactive) product experience and service experience)
17. “Use” Users For UX
“Buy” Customers For CX or Service
“Talk” Player For PX(Playful eXperience)(Saying/Typing and Respond)
“He has a
magic lamp
with a genie
inside, who
grants wishes.”
Design For Player’s Playful eXperience
(embodied (interactive) product experience and service experience)
Design For Customer eXperience or Service experience
Design For User eXperience or (Interactive) product experience
18. “Talk” Player
(Saying/Typing and Respond)
“He has a
magic lamp
with a genie
inside, who
grants wishes.”
For PX(Playful eXperience)
• Creating a magic lamp with a genie(Craft a Personality);
Creating a genie-like UX
• Personalized god in a box; Era of IPA(Intelligent Personal
assistant)
• Conversation-as-a-Platform(CaaP); Software-as-a-Service
(SaaS); Platform-for-Everything
Design For Player’s Playful eXperience
(embodied (interactive) product experience and service experience)
Designing Conversations:
Conversational interfaces, Bot Interactions, Chatbot as
personalities
31. Reference: Dormehl, Luke (2014-04-03). The Formula: How Algorithms Solve all our Problems … and Create More. Ebury Publishing.
Quantified Self movement
Self-knowledge through numbers
(숫자를 통한 자기 이해)
Based upon speech patterns, the particular words they used, and even
details as seemingly trivial as whether they said “um” or “err” – and then
utilise these insights to put them through to the agent best suited for dealing
with their emotional needs?
(Chicago’s Mattersight Corporation does exactly that. Based on custom
algorithms, Mattersight calls its business “predictive behavioral routing”.)
32. Quantified Self movement
Self-knowledge through numbers
(숫자를 통한 자기 이해)
Based upon speech patterns, the particular words they used, and even details as seemingly trivial as whether they said “um” or
“err” – and then utilise these insights to put them through to the agent best suited for dealing with their emotional needs?
(Chicago’s Mattersight Corporation does exactly that. Based on custom algorithms, Mattersight calls its business “predictive
behavioral routing”.)
The man behind Mattersight’s behavioural models is a clinical psychologist
named Dr Taibi Kahler. Kahler is the creator of a type of psychological
behavioural profiling called Process Communication.
What Kahler noticed was that certain predictable signs precede particular
incidents of distress, and that these distress signs are linked to specific
speech patterns. These, in turn, led to him developing profiles on the six
different personality types he saw recurring.
Reference: Dormehl, Luke (2014-04-03). The Formula: How Algorithms Solve all our Problems … and Create More. Ebury Publishing.
33. Quantified Self movement
Self-knowledge through numbers
(숫자를 통한 자기 이해)
Based upon speech patterns, the particular words they used, and even details as seemingly trivial as whether they said “um” or
“err” – and then utilise these insights to put them through to the agent best suited for dealing with their emotional needs?
(Chicago’s Mattersight Corporation does exactly that. Based on custom algorithms, Mattersight calls its business “predictive
behavioral routing”.)
The man behind Mattersight’s behavioural models is a clinical psychologist named Dr Taibi Kahler. Kahler is the creator of a
type of psychological behavioural profiling called Process Communication.
What Kahler noticed was that certain predictable signs precede particular incidents of distress, and that these distress signs are
linked to specific speech patterns. These, in turn, led to him developing profiles on the six different personality types he saw
recurring.
A person patched through to an individual with a similar personality type to
their own will have an average conversation length of five minutes, with a 92
percent problem-resolution rate. A caller paired up to a conflicting
personality type, on the other hand, will see their call length double to ten
minutes – while the problem-resolution rate tumbles to 47 percent.
Reference: Dormehl, Luke (2014-04-03). The Formula: How Algorithms Solve all our Problems … and Create More. Ebury Publishing.
34. Personality type Personality traits
How
common?
“Thinkers”
Thinkers view the world through data. Their primary way of dealing with
situations is based upon logical analysis of a situation. They have the potential
to become humourless and controlling.
1 in 4
people
“Rebels”
Rebels interact with the world based on reactions. They either love things or
hate them. Many innovators come from this group. Under pressure they can
be negative and blameful.
1 in 5
people
“Persisters”
Persisters filter everything through their opinions. Everything is measured up
against their world view. This describes the majority of politicians.
1 in 10
people
“Harmonisers”
Harmonisers deal with everything in terms of emotions and relationships.
Tight situations make this group overreactive.
3 in 10
people
“Promoters”
Promoters view everything through action. These are the salesmen of the
world, always looking to close a deal. They can be irrational and impulsive.
1 in 20
people
“Imaginers”
Imaginers deal in unfocused thought and reflection. These people operate in
vivid internal worlds and are likely to spot patterns where others cannot.
1 in 10
people
Dr Taibi Kahler’s the six different personality types
Reference: Dormehl, Luke (2014-04-03). The Formula: How Algorithms Solve all our Problems … and Create More. Ebury Publishing.
44. Sources:
• Gursimarjot Singh Walia, Gurjot Kaur Walia: Level of Asthma: A Numerical Approach based on Voice Profiling (2016)
• Pragnesh Parmar, Udhayabanu R. “Voice Fingerprinting: A Very Important Tool against Crime”. J Indian Acad Forensic Med. Jan- March 2012, Vol. 34, No.1, ISSN: 0971-0973.
• The voice of each person is different
because the anatomy of vocal cavity,
oral cavity, nasal cavity, and vocal
cords is specific to the individual.
• People in different countries, in fact,
people in different parts of the same
country, speak with different accents.
There are some people who run their
words together, and there are others
who talk with pauses between their
words.
• If a person is having some kind of
illness, such as cough, cold, fever etc.,
or feeling some kind of emotion, such
as happiness, sadness, stress, anxiety
etc., then their voice would be
different from what they sound when
they are normal.
비강(鼻腔)
구강(口腔)
성대(聲帶)
45. Source: Rita Singh, Joseph Keshet, Eduard Hovy: Profiling Hoax Callers (2016)
46. “지금 뉴스에 나오는
보이스피싱 사건은 지금
인터뷰하는 손자의
자작극이에요.
변조로 목소리를 다르게 하려고
애를 썼지만 손자의 호흡, 말투,
억양이 일치해요.
사람 목소리는 지문과 같아요.
손자가 자작극을 했을 확률이
99.9%에요.”
Source: 드라마 <보이스>
47. In police and Forensic Scientists,
sometimes voice is the only clue available in identifying the criminal.
Source: Gursimarjot Singh Walia, Gurjot Kaur Walia: Level of Asthma: A Numerical Approach based on Voice Profiling (2016)
48. “대표적인 ‘오원춘
사건‘.
피해자가 112에 죽기
직전에 전화했는데,
결국 시간이
지연되면서 다음날
아침 시신으로 발견.
심리분석이 가능한
‘보이스
프로파일러'가 그
전화를 받았다면
현장에 돌입할 수
있었을 것이다.”
Source: 드라마 <보이스> 홍보영상 https://www.facebook.com/CJTVING/videos/1385055181535656/
53. Source: Rose, Phil.: Where the Science Ends and the Law Begins: Theory and Reality in Likelihood Ratio-based Forensic Voice Comparison.(2012)
• Figure 1 shows the F0 realising the [H.L.LH] intonational pitch of the offender, aligned with its
wideband spectrogram.
• F0 on not can be seen to drop from about 200 Hz to 175 Hz; whence it drops further on the
nucleus of too to about 125 Hz.
• The F0 shows a small ca. 15 Hz increase from its minimum value of 125 Hz in the /b/ hold,
and rises on the nucleus of bad with a slightly convex contour from about 145 Hz to peak at
about 185 Hz.
54. Source: Rose, Phil.: Where the Science Ends and the Law Begins: Theory and Reality in Likelihood Ratio-based Forensic Voice Comparison.(2012)
• Figure 2 compares the offender F0 with the F0 of the suspect’s 15 not too bad
utterances.
• The similarity is considerable, with the offender’s F0 time-course lying completely
within, and in some places almost exactly in the middle of, the suspect’s distribution.
• Note too the suspect’s use of both H and L on not.
55. • Table shows the parameters that can be extracted using voice analysis, and the
information that can be extracted from those voice parameters.
Sources:
• Gursimarjot Singh Walia, Gurjot Kaur Walia: Level of Asthma: A Numerical Approach based on Voice Profiling (2016)
• Khushboo Batra, Swati Bhasin, Amandeep Singh. “ Acoustic Analysis of voice samples to differentiate Healthy and Asthmatic persons”. International Journal of Engineering and Computer Science,
ISSN 2319-7242, Volume 4, Issue 7, July 2015, Page No. 13161-13164.
• Pragnesh Parmar, Udhayabanu R. “Voice Fingerprinting: A Very Important Tool against Crime”. J Indian Acad Forensic Med. Jan- March 2012, Vol. 34, No.1, ISSN: 0971-0973.
※ Extraction of voice parameters
The above parameters were extracted using the MDVP (Model 5105, KayPENTAX) tool of CSL (Model 4500, KayPENTAX) system.
Fo: Average Fundamental Frequency
Jitt: Jitter (%)
Shim: Shimmer (%)
vFo: Coefficient of fundamental frequency variation
DUV: Degree of Voiceless
DSH: Degree of Sub-Harmonics
SPI: Soft Phonation Index
DVB: Degree of Voice Breaks
NHR: Noise-to-Harmonic Ratio
PPQ: Pitch Period Perturbation Quotient (%)
RAP: Relative Average Perturbation (%)
To: Average Pitch Period
58. Sources:
• Khushboo Batra, Swati Bhasin, Amandeep Singh: Acoustic Analysis of voice samples to differentiate Healthy and Asthmatic persons(2015)
• Rachna, Dinesh Singh, Vikas: FEATURE EXTRACTION FROM ASTHMA PATIENT’S VOICE USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS(2014)
• Saloni, R. K. Sharma, and A. K. Gupta. "Disease detection using voice analysis: a review." International Journal of Medical Engineering and Informatics 6.3(2014): 189-209.
• Sonu, R. K. Sharma “Disease detection using analysis of voice”, TECHNIA – International Journal of Computing Science and Communication Technologies, VOL.4 NO. 2, January 2012.
• Speech is produced
by vocal folds. It
involves the
interaction of various
body parts*. It can
hurt the sound
quality of the voice.
• Asthma is a lung
disease that affects
airflow to and fro
from lungs. A
whistling sound comes
when asthmatic
patient breathes.
* This includes various
components like
abdominal, ribcage, lungs,
pharynx, oral cavity and
nose and each performs its
own function in speech
production.
59. Sources:
• Gursimarjot Singh Walia, Gurjot Kaur Walia: Level of Asthma: A Numerical Approach based on Voice Profiling (2016)
• Khushboo Batra, Swati Bhasin, Amandeep Singh. “ Acoustic Analysis of voice samples to differentiate Healthy and Asthmatic persons”. International Journal of Engineering and Computer Science,
ISSN 2319-7242, Volume 4, Issue 7, July 2015, Page No. 13161-13164.
• There are several voice
pathologic disorders related
with nasal, neural,
respiratory and larynx
diseases. (코, 신경, 호흡, 후두
관련 질병)
• As a result, analysis and
diagnosis of vocal disorders
has become an important
medical procedure.
66. Source: Dave Winsborough and Tomas Chamorro-Premuzic: Talent Identification in the Digital World: New Talent Signals and the Future of HR Assessment(2016)