the google-ita software deal - can mobile voice recognition spark travel search innovation? - oct...

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The Google – ITA Software deal

How Android voice integration with ITA can

drive travel innovation and create a more

effective consumer experience

October 2010 Alford Strategic Development

http://linkd.in/10BF9l5

http://www.maketravelbetter.com/


Alford Strategic Development

How it could work – the basic process

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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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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





Why timing is right for Google’s opportunity

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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:





Potential vs current mobile search experience

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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





Potential vs mobile app download experience

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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 platform Speech platform Mobile platform

Siri iPhone

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





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.





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





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

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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

AB

I R

esearc

h

Data set vocabulary size and relative complexity by speech type

Appendix - Travel planning ideal for speech recognition





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





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





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





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




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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



the google-ita software deal - can mobile voice recognition spark travel search innovation? - oct...

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