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Doomed to Vanish? Voice, Tech & AI - Kantar
- 1. Voice, Tech & AI Cynthia Vega & Tom Evans Doomed to Vanish?
- 2. 2
- 4. Tomorrow? 4
- 5. Meet… 5 ` UNIFY ENRICH & STRUCTURE TRANSFORM VISUALISE
- 6. Great… 6
- 7. … not so great! 7
- 8. How can we help STAN detect complex and nuanced human emotions and cognitive states? 8
- 9. How do we make our non-human member of the team emotion aware? 9
- 11. 11 PERSONALITY They are excitable, sentimental and social. They are hedonistic: they feel their desires strongly and are easily tempted by them. They are emotionally aware: they are aware of their feelings and how to express them. And they are modest: they are uncomfortable being the centre of attention. Their choices are driven by a desire for belongingness.
- 12. What is the state of voice today? Not what is said, but how it is said. Paralinguistics, tone, loudness, tempo, and voice quality. 12
- 13. Royal Bank of Scotland | Case Study Innovator: Pete Evans, Innovation Strategy & Insight at RBS The Test 13
- 14. What did we do? 14
- 15. 15 90% Similar to Qualitative STAN Human Systematic Classification Plethora of verbatims Non Opinionated Opinionated Thorough Quantification Visual Focus on Text Focused Insights Exhaustive Reporting Faster & Cheaper
- 17. 1 DATA TRANSFORMATION & STRUCTURE Be future focused, think about the outcome and scalability 17
- 18. 2 IDENTIFICATION & CLASSIFICATION Analysis by sentence works best for voice 18
- 19. 3 QUANTIFICATION OF TOPICS Have an iterative approach with a human in the loop 19
- 20. 4 RESULTS INTERPRETATION Scale what you can but top up with qualitative to gain richness 20
- 21. 5 HOW HUMAN & MEANINGFUL IS VOICE TODAY? Limited emotions, human intervention needed but fit for certain purposes 21
- 22. 22 Purpose People Data Tech What are the learnings?
- 23. What does it take to succeed? Machine Human
- 24. 1 G E T R I D O F B L AC K B O X E S Data Science and Analytics is exciting. Many don’t speak the same language so try to demystify to gain traction and support on your projects. Show the human behind the machine. 2 T H I N K B U S I N E S S The more relevant the data is for the business, the more relevant you are for the business. Focus on meaningful insights, start with a business question in mind. 3 T E L L S TO R I E S Data analysis is now the domain of machines. Machines can’t create stories, use frameworks or understand clients (YET), you can! Not doomed to vanish, to gain a seat at the table… 24
Editor's Notes
- Voice Tech & AI – Changing the way brands interact with customer conversations There has always been something that gets in the way of our relationship with technology: the keyboard, the mouse, the screen. We’re now ready to analyse the most natural and intuitive form of interaction—the voice. RBS wanted to understand how their senior management felt about their business purpose, goals and strategy and so Kantar’s Analytics Practice used Artificial Intelligence to: structure voice data at scale and decode emotion from voice.
- https://www.forbes.com/sites/quora/2018/01/18/technology-has-already-taken-over-90-of-the-jobs-humans-used-to-do/#111d24071bdd https://www.express.co.uk/news/science/1018786/Artificial-intelligence-AI-robots-replace-humans-machines-take-over-jobs https://www.forbes.com/sites/forbestechcouncil/2018/02/26/artificial-intelligence-will-take-your-job-what-you-can-do-today-to-protect-it-tomorrow/#5320a5e04f27 https://hbr.org/2018/01/the-question-with-ai-isnt-whether-well-lose-our-jobs-its-how-much-well-get-paid
- Imagine if we could humanize technology
- Imagine if we could humanize technology
- https://affectiva.github.io/jssdk-giphy-demo/
- Alexa we have done in the past but it is more about the fact that we are moving to actual emotions on voice.
- Alexa we have done in the past but it is more about the fact that we are moving to actual emotions on voice.
- New: Speech Analysis Humans communicate and read emotions in a number of ways: facial expressions, speech, gestures and more. Our vision is to develop artificial emotion intelligence – Emotion AI that can detect emotion just the way humans do, from multiple channels. Our long term goal is to develop a “Multi-modal Emotion AI”, that combines analysis of both face and speech to provide richer insight into the human expression of emotion. As the first milestone towards our Multi-modal Emotion AI, we have now added speech capabilities to Emotion as a Service. The speech capabilities allow you to analyze a pre-recorded audio segment, such as an MP3 file, to identify emotion events and gender. The API analyzes not what is said, but how it is said, observing changes in speech paralinguistics, tone, loudness, tempo, and voice quality to distinguish speech events, emotions, and gender. The initial set of metrics include: Laughing – The action or sound of laughing. Anger/Irritation – A strong expression of displeasure, hostility, irritation or frustration. Arousal – The degree of alertness, excitement, or engagement produced by the object’s of emotion. Gender – The human perception of gender expression (Male/Female). The output file provides the analysis on speech events occurring in the audio segment every few hundred milliseconds and not just at the end of the entire utterance. Note that since speech is still in beta, the Emotion as a Service visualization is only available for facial analysis at this point in time.
- Alexa we have done in the past but it is more about the fact that we are moving to actual emotions on voice.
- https://affectiva.github.io/jssdk-giphy-demo/