This document summarizes a presentation on practical applications of speech technology. It discusses speech recognition, text-to-speech, biometrics, and data analytics. For speech recognition, call centers have excellent standardized systems while dictation and personalized answers are more expensive. Text-to-speech requires understanding language and translating terms. Speaker identification and characterization are practical using biometrics but verification is still rare. Data analytics through mining is useful but not real-time. The document also lists sponsors of the conference.

1. 2009 | Westergasfabriek | Amsterdam | http://eComm.ec

2. Practical Edge of
Speech Technology
Moshe Yudkowsky
www.Disaggregate.com
2

3. “Practical” is Relative
Affordable
Schedule
Achievable
3

4. Core Technology: Speech
Recognition (ASR), Text-
Engines to-Speech (TTS),
Biometrics, Thynometrics
(emotions)
Data mining, problem
Analytics
discovery
4

5. Two 20-second Exercises
5

6. Two 20-second Exercises
Exercise 1
Travel Agency
Automated
Reservations
5

7. Two 20-second Exercises
Exercise 1 Exercise 2
Travel Agency Twitter Update
Automated of eComm
Reservations Conference
5

8. Lessons
Exercise 1 Exercise 2
6

9. Lessons
Exercise 1 Exercise 2
Everyone has the
same & simple
answers
Call centers;
6

10. Lessons
Exercise 1 Exercise 2
Everyone has the
same & simple
answers
Call centers;
standard device
commands
Speaker
6

11. Lessons
Exercise 1 Exercise 2
Everyone has the
same & simple
answers
Call centers;
standard device
commands
Speaker
Speaker
Independent
6

12. Lessons
Exercise 1 Exercise 2
Everyone has the
Highly Personal
same & simple
Answers
answers
Call centers;
standard device
commands
Speaker
Speaker
Independent
6

13. Lessons
Exercise 1 Exercise 2
Everyone has the
Highly Personal
same & simple
Answers
answers
Call centers;
Dictation; voice
standard device
search
commands
Speaker Speaker
Speaker
Independent
6

14. Lessons
Exercise 1 Exercise 2
Everyone has the
Highly Personal
same & simple
Answers
answers
Call centers;
Dictation; voice
standard device
search
commands
Speaker Speaker
Speaker
Dependent
Independent
or
6

15. Network Hardware for
Speaker Independent
7

16. Network-
based
systems:
Your
equipment
(“Premises”)

17. Network-
based
systems:
“Hosted”

18. Local Hardware
10

19. Device-
based
systems
ASR
Results
Local
Recogniti
on
Known text
Complex,
personal
text

20. Device-
based
systems:
Hybrid
Voice Results
Voice to
server,
data back
to device
Speaker
independent
(?) ASR

21. Engine
Speech Recognition (ASR)
s
Summary:
You can do almost anything — but
the more you do, the more you
pay.
13

22. Telephony ASR is excellent:
Inexpensiv “What city?”—
“Amsterdam”
“What is wrong with your
phone?” — “I dropped it
Very
on the floor, and the
expensive
screen is cracked, and
now I can’t see anything.”
14

23. Cautions
No such thing as “speech to text”
Speaker dependent comes closest
Voicemail to text: human assisted
Some telephone ASR is also human
assisted
15

24. Speaker Dependant
Desktop computers can do excellent
transcription, need corrections
Hand-held devices have more
memory & power → better ASR
16

25. Engine
Text-to-speech (TTS)
s
Summary:
Available in many languages,
reasonable quality, sometimes
difficult to understand.
17

26. 18

27. TTS requires language understanding
and specific jargon translation:
18

28. TTS requires language understanding
and specific jargon translation:
“Mr.” → “Mister”
18

29. TTS requires language understanding
and specific jargon translation:
“Mr.” → “Mister”
“bbl” →“Be Back Later
18

30. TTS requires language understanding
and specific jargon translation:
“Mr.” → “Mister”
“bbl” →“Be Back Later
“287 m” →“about 300 meters”
18

31. TTS requires language understanding
and specific jargon translation:
“Mr.” → “Mister”
“bbl” →“Be Back Later
“287 m” →“about 300 meters”
Custom voices available
18

32. Biometrics (Speaker
Engine Identification, Speaker
s Verification, Speaker
Characterization)
Summary:
Speaker verification practical but
still rare; speaker identification &
characterization practical and
secret
19

33. Speaker Verification (is that really
you?)
Available, practical
Rare in the US, more prevalent in
Australia, Israel, and Canada
Roadblocks: valid fear; fear of
biometrics; love of fingerprints;
only part of complete solution
20

34. •Speaker Identification (who are
you?)
•Speaker Characterization (what are
you?)
21

35. Analytic Data mining, problem
s discovery
Summary:
Surprising useful, expensive
22

36. Not a real-time process
Word searches, “speech to text”
Emotion detection by ASR (swearing)
and by thynometrics (pitch, volume)
23

37. About Disaggregate
Moshe Yudkowsky
Disaggregate
2952 W. Fargo
Chicago, IL 60645
+1 773 764 8727
www.Disaggregate.com

38. Headline Sponsor
Platinum Sponsors
Gold Sponsors
2009 | Westergasfabriek | Amsterdam | http://eComm.ec