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Voxware 2010

2. o Short answer- YES!
o Most failed voice implementations can be traced to the voice
recognizer software that “listens to” and interprets what the work
says.
o Ignoring the quality of the voice recognition software can lead to
a failed implementation

3.  Dozens of recognizers on the market today that
are designed for a “controlled” environment (little
to no background noise)
 Warehouses are the most challenging places for
a voice recognizer to work
 Warehouses also employ a diverse workforce
with different native tongues and accents
 If recognizer makes a mistake, worker must
repeat himself and productivity suffers

4. o Voxware offers 99.9% recognition accuracy with their
voice technology
o Voxware Integrated Speech Engine (VISE) designed
to operate in very noise settings without
compromising accuracy. VISE has been refined over
25 years
o Voxware ensures consistently high recognition rates
regardless of mobile device being used, language or
environmental circumstances

5. o In an apparel warehouse near
Atlanta, a Voxware voice
solution accurately recognizes
workers who speak five different
languages:
English, Spanish, Bosnian, Viet
namese, and Somali.

6.  Speaker Dependent Recognizers: recognizers are
“trained” to recognize the way a specific user says a
vocabulary of words. Speaker independent recognizers
do not require training.
 Speaker independent recognizers are widely used for
customer service applications (e.g. airline reservations)

7. o VISE leads users through a training session and creates a voice
profile for each person that is specific to that person’s way of
speaking (e.g. accents)
o Speaker dependent recognizers account for an increase in
accuracy from 95% to 99.9%. This difference is huge in terms of
ROI from voice implementation.
o Training is time consuming, but research shows that time gained by
skipping training is lost in the first week of production use because
of mis-recognitions. Amounts to $20,000 of wasted worker time in a
medium size DC.

8.  Many voice recognizers try to block out background
noise with “noise-reducing” microphones.
 This does not ensure recognition accuracy. Why? DCs
have too much fluctuating noise for a “noise-reducing”
microphone to handle.

9.  VISE “listens” for background noise and eliminates it
which allows the recognizer to process only what the
worker said.
 According to Voxware, VISE has run in some of the
loudest operations imaginable (sawmills, airport
runways) and VISE still recognizes what users say with
near 100% accuracy.

10.  VISE is optimized to recognize phrases as opposed to
individual words.
 Continuous recognition enhances productivity because
workers can combine into one response what would ordinarily
take two to three interactions using a discrete word
recognizer.
 For example: “Check 457 Grab 6 Put to Alpha”. Other
systems would have to break this up into as many as three
interactions.

11.  VISE always “knows” what it is listening for.
 VISE ignores idle chitchat and waits for the expected
response. This is called “out of vocabulary rejection”.
 Recognizers that do not have “out of vocabulary rejection” will
interpret everything the user says as input, including overhead
paging that is loud enough.

12.  Anyone who knows VoiceXML (used to develop voice
applications) could create an application to interact with
VISE.
 Since VISE is open and standards-based, Voxware can
use a different VoiceXML recognizer if one is found that
could deliver better performance than VISE.

13.  Voxware’s software is hardware independent. Customers
are able to port their voice applications to new devices
without the need to rewrite any code.
 Voxware works with hardware manufacturers to help
them produce units with the requisite audio performance
and quality.