The Google-ITA Software Deal - can mobile voice recognition spark travel search innovation? - Oct 2010

Alford Strategic Development LinkedIn | (206) 734-7450 | www.maketravelbetter.com 1
The Google – ITA Software deal
How Android voice integration with ITA can
drive travel innovation and create a more
effective consumer experience
October 2010Alford Strategic
Development

Alford Strategic Development LinkedIn | (206) 734-7450 | www.maketravelbetter.com
Alford Strategic
Development
How it could work – the basic process
2
Google-ITA could convert spoken natural language terms to voice objects similar to drop down selections in
booking engines, which would then integrate with the fare search query data structure. Hotel search is a
great opportunity as well.
VXML 3.0
Analyzes voice input
using Speech Recognition
Grammar Specifications
(SRGS) to translate to text
SISR
Semantic Interpretation for
Speech Recognition extracts
key travel terms to convert to
voice objects
QPX fare search
Voice objects matched to
data structure for fare search
query and result processing
Google ITA Software
“Fly 1st-class Seattle to LA August 25 coming back August 28 on Virgin America”

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Development
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Introduction
Google’s $700 M acquisition of ITA has driven both good debate and sometimes illogical lobbying on risks
to airfare search integrity, competitor web traffic, advertising costs, and service to air industry customers.
There are positive opportunities, of course, and here we focus on a specific, less-publicized one that
could have a meaningful impact in the near future:
Based on our tests, Google’s Android voice recognition outperforms Bing, Vlingo and possibly other
speech solutions in travel-focused natural language search and is almost ready for mainstream adoption
Integrating with ITA’s airfare query structure (and potentially hotels) is a key opportunity for Google-ITA
to drive industry innovation and create a better consumer travel search experience

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Why timing is right for Google’s opportunity
4
Travel is a strong
candidate for mobile voice
search and multimodal
engagement
Google-ITA will have the
tools to execute it
Speech recognition has seemed promising for years, but has mainly been limited to electronic voice
menus…so what technology and consumer forces are aligning with Google capabilities to enable this now?
Travel lends well to speech
recognition – demonstrated in
Defense Dept tests 1991-1995
Smartphone penetration,
processing power, and dual
microphones growing rapidly
Cloud enables network-based
speech to process larger
vocabularies than embedded apps
Travel planning requires relatively
small grammar sets
Android currently outperforms
Microsoft and Vlingo for natural
language voice search of travel terms
In a short period of time, 25% of
Android searches are already Voice
Google has invested heavily to train
speech algorithms
Mobile and Tablet search should be
15% of searches by 2013-2014
Consumers rapidly adopting voice in
local search, car navigation, and
platforms like Microsoft Kinect
Technology elements
Consumer adoption and
Google advantages

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• Cutting multiple inefficient and redundant steps out of today’s mobile search and travel intermediary experience
• Enabling consumers to bypass OTA and Metasearch competitors, who could not easily replicate it without
considerable capital investment
• Encouraging competitors to innovate and find other ways to improve and bring their mobile products to consumers
• Prompting Microsoft and Apple to leverage their mobile platforms and alter the intermediary landscape further
Potential high-level impact
In a sense, this is a component of a larger potential Google PC and mobile metasearch play, but it
also impacts a key area of consumer experience needing improvement, and implications of leveraging
the Android platform could include:

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Potential vs current mobile search experience
6
Speak or Type
to search
Compared to today’s mobile search experience, voice search with fare integration would be much faster
than inconsistent text links due to SEM/SEO manipulation, redundant steps, and multiple drop-down or
typing fields
Speak or Touch to
select airline
Speak or Touch to
select flight
Airline / OTA
Booking
path
Potential Voice
search to
booking path
Airline / OTA
Booking
path
Speak or type
to search
Re-select
“Flight”
Inefficient
text links
Re-enter terms in multiple fields Flight result
matrix or list
Current mobile
search to OTA
mobile path
Note: Expedia used for demonstration of general steps, not to imply variance in quality relative to other OTA paths

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Potential vs mobile app download experience
7
Open app Flight result
matrix or list
Flight detail Choose OTA
or supplier
Comparing to downloaded mobile apps, current metasearch or OTA apps also require several additional
steps, including multiple drop-down or typing fields and additional site selections
Multiple text, calendar,
or drop-down fields
Note: Kayak used for demonstration of general steps, not to imply variance in quality relative to other metasearch apps
Current
metasearch
mobile app path
Potential Voice
search to
booking path
Speak or Type
to search
Speak or Touch to
select airline
Speak or Touch to
select flight
Airline / OTA
Booking
path
Airline / OTA
Booking
path

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Competitive implications
Only Google-ITA could achieve proprietary voice and fare technology integration, but Microsoft and Apple
own mobile and speech platforms with a key advantage in interfacing voice search directly with consumers.
OTA and Metasearch players are at risk if Google, Microsoft or Apple leverage ownership of mobile
platforms and would also need to partner with Vlingo, Loquendo, Nuance or others to embed speech.
Travelport ePricing
Amadeus Meta-pricer
Sabre
Vayant
Everbread
Expedia BFS
(if productized)
Fare query platformSpeech platformMobile platform
SiriiPhone
MSFT Speech /
Tellme
Windows Phone 7
Android Android ITA Software
Vlingo
Nuance
Loquendo
Promptu
MSFT API
Android SDK
None
Must distribute apps
or gain search traffic
through Android,
WP7 and iPhone
platforms

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Sample testing shows Android advantage and challenges
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Sample - “Fly 1st-class Seattle to LA August 25 coming back August 28 on Virgin America”
To evaluate the readiness of voice search platforms to handle travel-specific natural language input
strings, we tested 140 searches on Android, Bing (Windows Phone 7), and Vlingo speech platforms (see
Appendix for specific samples).
Our tests indicate Android is currently more advanced for travel, while Bing performed fairly well and
Vlingo was poor.
Platform Noise level
Key term
success rate WER Queries
mean search
time (sec)
mean
words
mean error
words
Android loud - Starbucks 30% 29% 20 7.4 12.4 3.6
Android quiet - office 85% 4% 20 3.7 13.2 0.6
Windows Phone 7 quiet - office 70% 7% 20 4.9 13.2 0.9
Vlingo quiet - office 10% 15% 20 3.5 13.2 2.0
Improved accuracy and search time is needed for practical use, especially in noisier environments, which we expect will
occur as dual microphone devices proliferate and cloud technology and speech training sets across all platforms improve.

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Appendices
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Appendices
Defense Advanced Research Project (DARPA) Travel Planning testing
Travel vocabulary size and relative speech complexity indicator
Lenati voice test sample data by phone platform
Projected mobile and tablet search query growth 2010-2014
Voice technology provider segments
Voice technology overview

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Appendix - Travel planning ideal for speech recognition
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Travel lends well to speech recognition, demonstrated by testing during the Defense Advanced Research
Project (DARPA) from 1991-1995
http://www.itl.nist.gov/iad/mig//publications/ASRhistory/index.html
DARPA’s Air travel planning test:
• Used multiple microphones
• Word Error Rate (WER) dropped from
20% to less than 3% in 5 years
• Measured to be as effective as human
interpretation

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Travel vocabulary consists mainly of finite sets of origins, destinations, dates, travel brands, and other key
categories rather than continuous or conversational speech that becomes incredibly complex in vocabulary,
meaning, nuance, and dialect.
• Therefore travel likely fits in the least
complex speech recognition type
• And reduces the complexity of
creating voice objects to match fare
search data structures
ABIResearch
Data set vocabulary size and relative complexity by speech type
Appendix - Travel planning ideal for speech recognition

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Appendix - Sample testing results
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Vlingo was worse than expected at this point
Android error rate was low, and in most cases, errors did not affect relevant terms needed to create Voice Objects
Spoken search string Text result WER
Search Virgin America flights from Seattle to San Francisco August 25 returning August 28 Search Virgin America flight from Seattle to San Francisco August 25 returning August 28 0%
I want to fly to Seattle from Washington DC on October 15, returning October 20 I want to fly to seattle from washington d c on october 15th returning october 20th 0%
I want to fly to Philadelphia from New Orleans on October 15, returning October 21 I want fly to philadelphia from new orleans on october 15th returning october 21st 7%
Fly from Seattle to Anchorage August 25 returning August 28 Alaska Airlines fly from seattle to anchorage august 25th returning august 28th alaska airlines 0%
Find flight Boston to Ft. Lauderdale August 25 returning August 28 JetBlue find flights boston to fort lauderdale august 25th returning august 28th jetblue 0%
Search round-trip flight St. Louis to Phoenix January 5 to 9 search round trip flights saint louis to phoenix january fifth 29 17%
Search Virgin America flights from Seattle to San Francisco August 25 returning August 28 virgin america flights from Seattle to San Francisco Aug 25th returning August 28th 0%
I want to fly to Seattle from Washington DC on October 15, returning October 20 12 flight to Seattle from Washington DC on October 15th, returning October 20th 27%
I want to fly to Philadelphia from New Orleans on October 15, returning October 21 Iwon to Fly to Philadelphia from New Orleans on October 15th raton October 20th 13%
Fly from Seattle to Anchorage August 25 returning August 28 Alaska Airlines flights from Seattle to Anchorage Aug 25th returning August 28 Alaska Airlines 0%
Find flight Boston to Ft. Lauderdale August 25 returning August 28 JetBlue flight Boston to Fort Lauderdale August 25th returning on 20 Eights Jet Blue 17%
Search round-trip flight St. Louis to Phoenix January 5 to 9 round trip flight St. Louis to Phoenix January 5th 29 17%
Search Virgin America flights from Seattle to San Francisco August 25 returning August 28 search virgin america flights from seattle to san francisco otis 25th return in august 28 14%
I want to fly to Seattle from Washington DC on October 15, returning October 20 I want to fly to seattle from washington dc on october 15th return in october 20th 7%
I want to fly to Philadelphia from New Orleans on October 15, returning October 21 I want to fly the philadelphia from new orleans on the kerber 15 return in october 21st 20%
Fly from Seattle to Anchorage August 25 returning August 28 Alaska Airlines fly from seattle to anchorage august 25th attorney in august 28 alaska airlines 8%
Find flight Boston to Ft. Lauderdale August 25 returning August 28 JetBlue flight boston the fort lauderdale august 25th return in august 28 jet blue 25%
Search round-trip flight St. Louis to Phoenix January 5 to 9 search round trip lake saint louis to phoenix january fifth 29 17%
Windows Phone 7 error rate was higher, but in many cases did not affect relevant terms

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Appendix - Voice will help drive mobile search growth
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• PC search should continue to dominate, but Mobile and
Tablet are also high-growth
• Google owns estimated 90% of mobile query share
• 25% of Android searches are already Voice
Mobile, Tablet and PC search queries 2010 - 2014
Both Google and Microsoft have invested heavily in Android and Windows-based Voice recognition in
anticipation of Mobile and Tablet search growth
Sources: Morgan Stanley; Microsoft

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Appendix - Voice technology overview
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VXML manages dialogue between person and device, and version 3.0 focuses on improved media
control and synchronization, identification and verification capability, improved extensibility, and
better multimodal input architectures via use of XHTML+Voice
VoiceXML
Speech Recognition
Grammar Specifications
Semantic Interpretation
for Speech Recognition
SRGS identifies the sentence structure expected in the human voice responses
SISR extracts key information from voice inputs to create voice objects for applications, and
examples include airfsearch integration with ITA data structures or converting phone-based
restaurant delivery order data into a format for order processing applications
Speech recognition is based on the Hidden Markov Model (HMM), with a statistical approach enabling the technology to be “trained,”
decision weighting adjusted to improve accuracy, and huge data sets ideally of examples of all variations of a spoken string.
Since it is unlikely any two spoken terms by different people will match, the model statistically determines what was most likely said,
and the process below takes place to enable spoken terms to convert to use for applications.

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Often preferred since response times can be faster than network-based speech and performance is not
subject to network connectivity.
Though phone technology will improve and many applications are suitable for embedded speech, large
vocabularies and spontaneous speech require processing and memory not yet available.
Virtually unlimited processing power enables more robust capabilities in handling large vocabulary
requirements, complex continuous speech processing, and natural language inputs.
Key challenges revolve around latency in data transfer, and a quick and accurate experience on mobile
devices. However, both should continue to improve consistently as technology improves.
Hybrid systems combine embedded and network-based processing, offering an advantage over a straight
network approach as the extraction performed on the device reduces complexity that can increase errors.
Network-based
Speech
Embedded
Speech
Hybrid or
Distributed
Speech
Speech recognition systems can be network (cloud)-based, embedded directly into phone operating systems, or a combination of both,
each of which carries advantages and disadvantages.
Appendix - Voice technology overview

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Appendix - Voice technology provider segments
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Microsoft Windows Phone 7
Google Android
Apple iPhone / iPad (iOS)
Vlingo
Nuance
Loquendo
Promptu
IBM
AT&T Watson
Mobile platforms with proprietary speech
Speech providers with mobile apps
Speech technology infrastructure
• By owning the phone platforms and default search applications, they
could essentially control the voice search experience and market
• But by owning ITA Software, only Google could create seamless voice
search integration with airfare and potentially hotel data structures
• Travel intermediaries or suppliers could partner with them, or they
could develop their own travel-specific applications
• Must market to travelers to bypass default mobile search applications
• Promptu owns ProntoTreno, a voice-interactive train services and
booking application in Italy, and Flights2Go, an airline services app
• AT&T Watson technology is a key component of Vlingo
• Travel intermediaries or suppliers could partner with them

Alford Strategic Development LinkedIn | (206) 734-7450 | www.maketravelbetter.com 18
About Jonathan Alford
With 16+ years of experience in consumer and business travel, hospitality, and retail, Jonathan’s work
centers on dual principles of driving a better traveler experience and advancing travel industry/company
economic models.
Recent focus includes creating visionary product and platform strategies for global corporate travel firms,
leveraging new mobile/tablet platform capabilities and themes of "consumerization" to improve traveler
experience, drive economic value to offset rising travel costs, and deliver enhanced duty of care to
mitigate travel risks as emerging economies shift.
Recognized thought leadership influencing disruption of airline industry WiFi and digital entertainment
(manifested in current advancements in both in-flight and "outside the flight" experience) and promoting
voice recognition technology impact in mobile travel search and itinerary management.
Jonathan was awarded an Olympic Order of Excellence for executive program and Games-wide
operations management at the 2002 Winter Olympics, judged best-managed Games in history.
He is a graduate of The Johnson School at Cornell University and the University of Virginia.
About Alford Strategic Development

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The Google-ITA Software Deal - can mobile voice recognition spark travel search innovation? - Oct 2010