Future of Voice UX: Everywhere and 合流(Voice 관련 연구 탐색 및 Voice 서비스 통찰 중심으로)

Future of
Voice UX:
Everywhere and 합류(合流)
(Voice 관련 연구 탐색 및
Voice 서비스 통찰 중심으로)
2017
Billy(최병호)/BillyChoi@Gmail.com
중앙대학교 교수
홍익대학교 영상대학원(HCI개론 강의)/
연세대학교 공학대학원(서비스디자인경영 강의)/
성균관대학교 일반대학원 휴먼ICT융합학과(교수)/
HEDcentric UX미래융합전략연구소(연구소장)
InnoUX(대표이사)
Research Data: http://www.slideshare.net/BillyChoi/
Blog: http://blog.naver.com/soularchitec
Twitter/Facebook: ILOVEHCI

“넌 세상 바꿔보겠다고 이 짓거리
하냐? 난 아닌데!
나는 사람 살려보겠다고 이 짓거리
하는 거야.
죽어가는 사람 앞에서 그
순간만큼은 내가 마지노선이니까.
내가 물러서면 그 사람은 죽는
거고, 내가 포기하지 않고 조금만
노력하면 그 사람 사는거고.
낭만!”
Source: 드라마 <낭만닥터 김사부> 20회

© 2017 Billy All rights reserved.Future of Voice UX: Everywhere and 합류(合流)
(Voice 관련 연구 탐색 및 Voice 서비스 통찰 중심으로)
Table of Contents
• Voice Fingerprinting & Forensic Science
• Voice Recognition & Smart Healthcare
• Voice Profiling & Digital Interviewing
• Voice Profiling & Call center
2

Voice
Fingerprinting &
Forensic Science

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)

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)

Source: Rita Singh, Joseph Keshet, Eduard Hovy: Profiling Hoax Callers (2016)

“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>

“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/

“범죄 현장의 ‘소리＇를
통해 많은 정보를
수집하는 것은 굉장히
좋은 수사의 방향이 될
수 있다.”
Source: 드라마 <보이스> 홍보영상 https://www.facebook.com/CJTVING/videos/1385055181535656/

FVC(forensic voice comparison)
• In forensic voice comparison(FVC),
 Speech recordings from an unknown voice, usually of an offender, are compared with recordings from a
known voice, usually the suspect.
• In the first type,
 The expert considers their aim to be to say how likely it is, given the evidence, that the suspect said the
incriminating speech.
• In the second type of FVC,
 The expert’s aim is seen as restricted to estimating the strength of the speech evidence with a
Likelihood Ratio(LR)?
 In other words, to estimate how much more likely the difference between the suspect and offender
speech samples is, assuming the offender sample has come from the suspect, rather than from
another randomly chosen speaker in the relevant population.
• For some time now,
 The use of a LR has been theoretically recognised as the correct logical framework for the evaluation of
forensic evidence.
Source: Rose, Phil.: Where the Science Ends and the Law Begins: Theory and Reality in Likelihood Ratio-based Forensic Voice Comparison.(2012)
References:
• Association of Forensic Science Providers: Standards for the formulation of evaluative forensic science expert opinion. Science & Justice 49, 161-164 (2009)
• Gonzalez-Rodriguez J., Rose P., Ramos, D., Torre, D. & Ortega-Garcia, J.: Emulating DNA: Rigorous Quantification of Evidential Weight in Transparent and Testable Forensic Speaker Recognition.
IEEE Trans. on Audio Speech and Language Processing 15(7), 2104-2115 (2007)
• Balding, D.J.: Weight of Evidence for Forensic DNA Profiles. Wiley, Chichester (2005)
• Aitken, C.G.G., Taroni, F.: Statistics and the Evaluation of Evidence for Forensic Scientists. Wiley, Chichester (2004)
12

LR(Likelihood Ratio) approach
• A crucial desideratum in forensic comparison science?
The accuracy (and more recently the precision) of a LR-based FVC(forensic voice
comparison) system are also straightforwardly tested.
• Apart from its correctness,
The LR approach has several other important properties.
It allows, for example, the combination of evidence of different types, nicely
demonstrated in the testing of both automatic and acoustic-phonetic features in
hybrid FVC systems.
The LR-based testing of other forensic evidence types is now following:
fingerprints, handwriting and SMS texting.
References:
• Morrison, G.S.: Measuring the Validity and Reliability of forensic likelihood-ratio systems. Science & Justice (51) 3, 91-98 (2011)
• Morrison: G.S. Forensic voice comparison and the paradigm shift. Science & Justice 49,298-308 (2009)
• Gonzalez-Rodriguez J., Drygajlo, A., Ramos-Castro, D., Garcia-Gomar, M., Ortega-Garcia, J.: Robust estimation, interpretation and assessment of likelihood ratios in forensic speaker recognition.
Computer Speech and Language 20, 331-355 (2006)
13

• 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.

• 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.

• Table shows the parameters that can be extracted using voice analysis, and the
information that can be extracted from those voice parameters.
Sources:
• 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

Voice 서비스 통찰
1. 공간 구분의 필요성
 폐쇄 공간 & 오픈 공간: 지속적으로 상주하는 특정인의 유무 기준으로 구분
2. 폐쇄 공간 내 서비스 예측 및 서비스 디자인
 범죄 예방 및 범죄 골든타임 지원 서비스
① 집이나 어린이집 등에서 가까운 관계의 상대에서 가해지는 일시적 또는
지속적으로 자행되는 범죄 관련 지원 서비스
② 외부인이 침입 시도 또는 침입하여 발생하는 범죄 관련 지원 서비스
 패턴과 Context 기반의 서비스 시나리오 디자인
 수요자와 공급자 모두 대상(일시 방문자 및 외부 침입자 포함)
 자동 대처와 그 외의 대처 상황 고려 필요
 다양한 생태계와 유기적인 공조 서비스 시나리오 디자인 필요
 보안 정책 및 사생활 침해 등 윤리 가이드라인 수립 필요
17

Voice 서비스 통찰(cont.)
3. 폐쇄 공간 내 공급자 제공 기술 예측
 범죄 예방 지원 기술
① 지속적으로 상주하는 특정인의 보이스 프로파일링 및 보이스 인증 기술
② 일시 방문자의 보이스 프로파일링
③ 폐쇄 공간 내 노이즈 프로파일링 및 이상 노이즈 중점 분석 기술
④ 행동패턴 누적 및 분석 기술(지속적인 모니터링이 가능한 관제 관련 기술 포함)
 범죄 골든타임 지원 기술
 영상인식 등 다른 기술과의 연계 지원 기술
18

Voice
Recognition &
Smart
Healthcare

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.

Sources:
• 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.

Asthma patient’s voice & Voice Recognition
22
Source: Rachna, Dinesh Singh, Vikas: FEATURE EXTRACTION FROM ASTHMA PATIENT’S VOICE USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS(2014)
In the above graphs an analysis of mean of five vowels (a,e,i,o,u) for males and
females are presented for various voice parameters like JITTER and SHIMMER of
different asthma and healthy persons are compared.

Asthma patient’s voice & Voice Recognition(cont.)
• Asthma has no cure, just it can be controlled. Major risk factors are bedding dust,
carpet, furniture dust, also family history or allergy.
• It can be controlled during asthma stages by doing long term meditation daily,
regular check up by doctor in case of serious patients, taking some drugs through
inhalers when asthma attack came etc.
• Further these extracted coefficients will be analyzed for finding similarities between
patients and normal persons.
23
Source: Rachna, Dinesh Singh, Vikas: FEATURE EXTRACTION FROM ASTHMA PATIENT’S VOICE USING MEL-FREQUENCY CEPSTRAL COEFFICIENTS(2014)

Voice Analysis of Parkinson Disease & Voice Recognition
24
Sources:
• Saloni, R. K. Sharma, Anil K. Gupta: Voice Analysis for Telediagnosis of Parkinson Disease Using Artificial Neural Networks and Support Vector Machines (2015)
• Max A. Little, P. E. Macsharry, E. J. Hunter, J.Sielman, L. O. Raming, “Suitability Of Dysophonia Measurements for Telemonitoring of Parkinson’s Disease, IEEE Transaction on biomedical engg,Vol.
56,2009, pp 1015-1022.

Acoustic Analysis of voice samples to differentiate Healthy
• A human voice is very closely related to the human health conditions, both physical
and mental. Changes in voice quality and pitch occur frequently in hormonal
imbalances or deficiencies.
• Through acoustic analysis, factors that affect the production mechanism of human
voice leads to the non-invasive diagnosis of diseases.
• The person’s voice suffering from any disease different from the healthy person in
some extent. As the various diseases like Parkinson, dysphonia, cardio-vascular,
dystharia & respiratory tract infection lay their impression on voice of a person.
• With the help of speech we will extract various information about the speaker,
gender, language, emotions health.
25
Sources:
• Khushboo Batra, Swati Bhasin, Amandeep Singh: Acoustic Analysis of voice samples to differentiate Healthy and Asthmatic persons(2015)
• Cyril R. Pernet et al. “The human voice areas: Spatial organization and inter-individual variability in temporal and extratemporal cortices”. Neuroimage 119(2015) 164-174.
• 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.
• Teixeira, João Paulo, and Paula Odete Fernandes. "Jitter, Shimmer and HNR Classification within Gender, Tones and Vowels in Healthy Voices." Procedia Technology 16 (2014): 1228-1237.
• Pati, Debadatta and SR Mahadeva Prasanna. "Speaker recognition from excitation source perspective." IETE Technical Review 27.2 (2010): 138-157.
• Peng, Ce, et al. "Pathological voice classification based on a single Vowel's acoustic features." Computer and Information Technology, 2007. CIT 2007. 7th IEEE International Conference on. IEEE,
2007.
• Friedrich S. Brodnitz. “Hormones and the Human Voice”, Bulletin of the New York Academy of Medicine 03/1971; 47(2):183-91.

Voice 서비스 통찰
1. 공간 구분의 필요성
 폐쇄 공간 & 오픈 공간: 지속적으로 상주하는 특정인의 유무 기준으로 구분
2. 폐쇄 공간 내 공급자 제공 기술 예측
 질병 예방 지원 기술
① 지속적으로 상주하는 특정인의 질병 프로파일링 및 보이스 인증 기술
② 외부인의 보이스 프로파일링으로 질병 예측 및 감염 여부 분석 기술
③ 폐쇄 공간 내/외 환경 분석 기술
④ 지속적인 모니터링이 가능한 관제 관련 기술
 골든타임 지원 기술
 공기 측정, 스마트 드러그 서비스, 영상인식 등 다른 기술과의 연계 지원 기술
26

Voice 서비스 통찰(cont.)
3. 폐쇄 공간 내 서비스 예측 및 서비스 디자인
 질병 예방 및 골든타임 지원 서비스
① 집이나 어린이집 등에서 질병 (감염) 유발 환경 모니터링
② 경증, 중증 환자의 경우, 지속적인 건강 상태 모니터링
 패턴과 Context 기반의 서비스 시나리오 디자인
 수요자와 공급자 모두 대상(외부인 포함)
 자동 대처와 그 외의 대처 상황 고려 필요
 다양한 생태계와 유기적인 공조 서비스 시나리오 디자인 필요
 보안 정책 및 사생활 침해 등 윤리 가이드라인 수립 필요
27

Voice Profiling &
Digital
Interviewing

Source: Dave Winsborough and Tomas Chamorro-Premuzic: Talent Identification in the Digital World: New Talent Signals and the Future of HR Assessment(2016)

New Talent Signals and the Future of HR Assessment
• Through the addition of innovations, such as text analytics and algorithmic reading
of voice-generated emotions, a wider universe of talent signals can be sampled.
• In the case of voice mining, candidates’ speech patterns are compared with an
“attractive” exemplar, derived from the voice patterns of high performing employees.
Undesirable candidate voices are eliminated from the context, and those who fit
move to the next round.
• More recent developments include video-mediated scenario-based questions,
images, video, and work samples and automated reading of micro-emotions
during the interview.
• For example, Hirevue.com, a leading provider of digital interview technologies,
employs coding challenges to screen software engineers for their software writing
ability. Likewise, Uber uses similar tools to test and evaluate potential drivers
exclusively via their smartphones.
30
Sources:
• Tomas Chamorro-Premuzic, Dave Winsborough, Ryne A. Sherman, Robert Hogan: New Talent Signals: Shiny New Objects or a Brave New World?(2016)
• Dave Winsborough and Tomas Chamorro-Premuzic: Talent Identification in the Digital World: New Talent Signals and the Future of HR Assessment(2016)

Source: Daniel Chen, Yosh Halberstam, Alan Yu: Covering: Mutable Characteristics and Perceptions of Voice in the U.S. Supreme Court (2016)

The emotion detection based on speech signal analyses features
1. Pitch
2. Energy (computing Teager Energy Operator – TEO)
3. Energy fluctuation
4. Average level crossing rate (ALCR)
5. Extrema based signal track length (ESTL)
6. Liner prediction cepstrum coefficients (LPCC)
7. Mel frequency cestrum coefficients (MFCC)
8. Formants
9. Consonant vowel transition
32
Source: Batalla, J.M., Mastorakis, G., Mavromoustakis, C.X., Pallis, E.: Beyond the Internet of Things: Everything Interconnected(2017)

The OCC model
Source: Ortony, A., Clore, G.L., & Collins A. (1990). The Cognitive Structure of Emotions Cambridge Univ. Press

Appendix. Emotional attributions significantly associated with acoustic parameters
34
Source: Klaus R. Scherer: Expression of Emotion in Voice and Music(1995)

Appendix. Microexpressions
• Based on Ekman’s research on emotions (Ekman, 1993), the security sector has
developed microexpression detection and analysis technology to enhance the
accuracy of interrogation techniques for identifying deception (Ryan, Cohn, & Lucey,
2009).
 Ekman, P. (1993). Facial expression and emotion. The American Psychologist, 48(4), 384–392. doi:10.1037/0003-066X.48.4.384
 Ryan, A., Cohn, J., & Lucey, S. (2009). Automated facial expression recognition system. 43rd Annual 2009 International
Carnahan Conference on Security Technologies, 172–177. doi:10.1109/CCST.2009.5335546
• The recent creation of large databases of microexpressions (Yan, Wang, Liu, Wu, &
Fu, 2014) is likely to facilitate the standardization and validation of these methods.
Yan, W. J., Wang, S. J., Liu, Y. J., Wu, Q., & Fu, X. (2014). For micro-expression recognition: Database and suggestions.
Neurocomputing, 136, 82–87. doi:10.1016/j.neucom.2014.01.029
• Beyond using automated emotion reading, new research aims to correlate facial
features and habitual expression with personality (Kosinski, 2016).
Kosinski, M. (2016, January). Mining big data to understand the link between facial features and personality. Paper
presented at the 17th Annual Convention of the Society of Personality and Social Psychology, San Diego, CA.
35
Sources:
• Tomas Chamorro-Premuzic, Dave Winsborough, Ryne A. Sherman, Robert Hogan: New Talent Signals: Shiny New Objects or a Brave New World?(2016)
• Dave Winsborough and Tomas Chamorro-Premuzic: Talent Identification in the Digital World: New Talent Signals and the Future of HR Assessment(2016)

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”.)

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.

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.

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

Similarity-attraction(유사성-매력 전략)(1/4)
• Personality traits(성격 특성); 5살 무렵에 형성
• Four Personalities
41
비판형 외향형
내향형 수용형
냉정형 다정형
순응형
지배형
협력
* Source: Clifford Nass & Corina Yen, 2010

• This is a reproduction of one of the most famous of the Tiffany stained-glass pieces—
the colors are absolutely sensational! This first-class, handmade copper-foiled stained-
glass shade is over six and one-half inches in diameter and over five inches tall. I am
sure that this gorgeous lamp will accent any environment and bring a classic touch of
the past to a stylish present. It is guaranteed to be in excellent condition! I very highly
recommend it.
• This is a reproduction of a Tiffany stained-glass piece. The colors are quite rich. The
handmade copper-foiled stained-glass shade is about six and one-half inches in
diameter and five inches tall.
42

• You should definitely select option A instead of option B. There are at least six
reasons why this is the right option. I am 90 percent confident of this assessment.
• Perhaps you should select option A instead of option B? It seems like there are
reasons why this might be the right choice. I am 40 percent confident of this
assessment.
43

• Similarity-attraction affects people to such a degree that they feel positive toward
not only similar people but also anything associated with those similar people. For
example, in the experiment, not only did participants like the sellers who were similar
to themselves, they also felt more positive about the items associated with the
similar sellers.(유사성-매력 효과는 긍정적 감정 뿐만 아니라 유대감 유발. 심지어
성격이 비슷한 판매자가 경매에 올린 제품까지 선호)
• 외향성 음성과 내향성 음성 동일 적용; 음량, 음역, 음성 속도; 성격과 음성의 일관성
중요하게 판단함
• When the introduction to a computer-based “Entertainment Guide” matched users’
personalities, users found the recommended music to be significantly better, even
though the recommendations themselves were identical.(동일 음악을 추천하여도
서비스 도입부가 자신의 성격에 부합하면 선호 발생)
44

“매 순간 정답을 찾을 수는 없지만,
그래도 김사부는 항상 그렇게 말했다.
우리가 왜 사는지,
무엇 때문에 사는지에 대한
질문하는 것을 포기하지 마라.
그 질문을 포기하는 순간,
우리의 낭만도 끝이 나는 거다.
알았냐?
라고 말이다.”
Source: 드라마 <낭만닥터 김사부> 20회

“거기서는 생명존중이나
인간의 존엄 같은 것은 없어.
그냥 하루하루 죽지 않고
버티는 거. 그것만 생각해.
같은 시간을 살아가는데,
그런 세상이 존재한다는 거.
믿겨져?”
Source: 드라마 <낭만닥터 김사부:> ‘Appendix. 그 모든 것의 시작’ 편

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Future of Voice UX: Everywhere and 合流(Voice 관련 연구 탐색 및 Voice 서비스 통찰 중심으로)

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