The market is already demonstrating strong value in the home for voice-activated AI, but the work environment is yet to catch up. Omar will explain why voice-activated AI is the most important development to come to the workplace. He will pull from his experiences creating Eva, the first enterprise voice assistant focused on making meetings more actionable, and dive specifically into the challenges of ASR (Automatic Speech Recognition), NLP and neural networks in creating these kinds of voice-activated assistants. He will share how his team have overcome these challenges.

1. E N T E R P R I S E V O I C E A IE N T E R P R I S E V O I C E A I

2. Exoskeletons, not
Robots
• Four attributes of exoskeletons
– Enhance not replace
– Collective intelligence
– Fits your workflow
– Human-in-the-loop (optional)
2

3. The most adopted form of enterprise collaboration
3
high
low high
Email
Meetings
(Voice)
low
Employee time spent
IM
Information
Generation
Size of bubble represents
activation opportunity
lacks activation
Enterprise
Apps

4. 3
$1,000
AVG. LABOR COST PER 60
MINUTE MEETING
$75M
FORTUNE 50 COMPANY
WASTED LABOR
9B
US MEETINGS PER YEAR
(100B GLOBALLY)
37%
TIME SPENT IN
MEETINGS

5. voicera = voice collaboration
• Connect what you say with you what you do
• meet eva, your in-meeting AI assistant that takes notes
5
step 1: call or invite eva@voicera.com to your meetings
step 2: interact through voice queues or “taps”
step 3: review email and share through Voicera

6. secular trends
6
Enterprise
Voice Collaboration
Consumer
“Gartner predicts that by 2020, 60%
of meetings with three or more
participants will involve a virtual
assistant.”

7. 7
Agenda
Actions
Decisions
Artifacts
Feedback
Meeting Threads
horizontal use: collaborate and share information with clarity…
conversations inbox

8. Post Meeting Inbox View
8

9. Post Meeting Inbox View
9

10. A different type of competitive advantage
10
• Oracle Data Cloud & Classic Data Network Effects
• AI can create a compounding competitive advantage*
More Data
Sellers
More Data
Buyers
Better
Monetization
Network
Effect
…but producing this type of advantage isn’t business as usual.
Better
Experience
More
interaction
data
Better
algorithmic
results
Deeper
preferences
learned
Compounding
advantage
*GGVC term

11. Building the data pipeline
• Bootstrap through acquiring data & labels
• Generate production data
• Process for accurate, continuous labels
(e.g. FP, TP, and FN)
• Compress learning cycles w/ model
automation:
– Creation
– Judgement
– Parameter tuning/learning
– Deployment
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12. Example: Key Word Spotting
• Goal: Utterance in which the keyword is spoken has higher
confidence than any other spoken utterance in which the
keyword is not spoken
• The most common measure to evaluate keyword spotters is
AUC (Area Under Precision & Recall Curve)
• Alternatively, we also use Recall @ Near 100% Precision
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13. Technical Challenges
• Telephony is the least common denominator
– 8K Sampling Rate
• A wide variety of microphones & meeting environments
• High Social Cost of False Triggers
• Online Decoding: Very Fast & Small footprint
• Handle different accents and pronunciations
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14. Avoiding Judgement Errors:
Survivor Bias Example
• A KWS creates FP, TP and misses FN
• FP & TP are easily labeled (FN are harder)
• Survivor bias misjudges performance of next candidate
• New algorithm has a bias for it for FP
– b/c it won’t generate the same false positives
– but it would generate its own false positives
• New algorithm has a bias against it for FN
– b/c it is judged against TP and fails >0%
– but it could accurately identify previous algorithms FN
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15. Results
• We train a number of models for various keywords
• On average we achieve:
– A precision of ~0.0005%
false trigger every 3 (1-hour meetings)
– A recall of ~90%:
1 of 10 voice commands missed
• The results vary dramatically based on
environments
• Our online training constantly trains
• Please visit: http://voicera.com to signup and use
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Performance over time
recall
precision

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