Autonomy EnterpriseSpeech Analytics

IndexAutonomy Enterprise Speech Analytics 1

Understanding Speech 1Approaches to Speech Analytics 2Phonetic Searching 3Word Spotting 3Conceptual Understanding 4Language Independent Voice Analysis 5Advanced Analytics 5Automatic Query Guidance 6Hot and Breaking Topics 6Clustering 6Script Adherence 6Trend Analysis 6Sentiment Analysis 6Multi-Channel Interaction Analysis 7

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Autonomy Enterprise Speech AnalyticsKnowing the topics, sentiments and concepts that are being discussed in your business is critical to understanding and

responding to the critical factors that undoubtedly affect market presence and profitability. By analyzing voice information that

comes from routine customer interactions, voicemails, video, and other sources, speech analytics can have a profound impact

on the way businesses manage customer service, sales and marketing, development, business strategy, risk, and liability.

While voice recording and monitoring has become a mature market for many organizations, it is the ability to analyze and

understand speech that enables businesses to reach a higher level of development and strategy than cannot be achieved

through legacy speech technologies. Autonomy delivers meaning-based speech analytics to tap into enterprise audio

information and extract relevant and actionable business intelligence. Speech analytics can be applied in a wide range of

vertical markets for a variety of business purposes, including:

• CustomerIntelligence

• VoiceandVideoSurveillance

• RichMediaManagement

• RegulatoryCompliance

• RiskAnalysis

• eDiscoveryandLitigation

• FraudDetection

• SalesVerification

• DisputeResolution

Understanding SpeechInordertosearch,analyze,andretrievespeechinformationwithinthebusiness,analyticstechnologymustfirstbeable

to recognize and understand spoken communications. Because a speaker’s language, dialect, accent, or tone can affect

the way words and phrases sound, legacy speech recognition technologies often misinterpret what is being said. Speech

processing can be further complicated by external factors such as background noise, mode of communication, and the

quality of the recording.

Autonomy’s speech recognition engine accounts for the variability in speech by using a combination of acoustic models, a

language model, and a pronunciation dictionary to form a hypothesis of what is being said. The acoustic model allows the

speech engine to recognize the probability of a specific sound translating to a word or part of a word. The language model

builds upon this to enable the system to determine the probability of one word following another to produce an accurate

hypothesisofthespokenwords.Forexample,“thebogbarked”soundsverysimilarto“thedogbarked”,buttheprobability

of barked following dog is much greater than that of barked following bog. The language model can be adjusted to support

industry-specific words and phrases so that they are recognized as probable. As more and more interactions take place,

the system trains itself to recognize frequently used words and phrases and becomes more accurate over time.

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This meaning-based approach enables the speech engine to form an understanding of spoken information based on the

context of the interaction rather than relying on sound alone. By understanding the relationships that exist between words,

Autonomy’s technology can effectively discern between homophones, homonyms, and other linguistic complexities that

often lead to false positives with legacy methods.

Approaches to Speech AnalyticsSpeech technology has gone through several phases of innovation, each one building upon the limitations of previous

methods.IntelligentVoiceResponsesystemsbuiltintotelephonysystemsthatallowedcallerstopressorsayalimited

numberofkeywordssuchas“yes”and“no”thatwerealreadybuiltintothesystem.Speechtechnologywaseventually

able to recognize more complex words and phrases but had trouble segmenting words without distinct pauses in the

speech. Several phases of speech recognition followed, including phonetic indexing and word-spotting methods that

improved accuracy but often produced false-positives and missed potentially relevant information.

Inresponsetothechallengespresentedbyphonemeprocessingandwordspottingtechniques,languagemodelswere

developed to give a more accurate recognition rate for complex words and phrases by using a dictionary and a pre-defined

language model. Self-learning language models were introduced to automatically expand the system's vocabulary based

on commonly used words. Today, a combination of language models, acoustic models, and advanced algorithms are used

to understand the relationships that exist between words to form a conceptual understanding of their meaning. Autonomy

supports all methods for speech processing, including phonetic searching, word spotting, Boolean and parametric methods,

and conceptual understanding.

By understanding the relationships that exist between words, Autonomy’s technology can effectively filter through speech that often lead to false positives with legacy methods.

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Phonetic SearchingPhonemes are the smallest discreet sound-parts of language and form the

basic components of any word. Phonetic searching attempts to break down

words into their constituent phonemes and then match searched terms to

combinations of phonemes as they occur in the audio stream. While this

approach does not necessarily require full dictionary coverage as the user is

able to suggest alternative pronunciation via different text-compositions, it is

limited in its accuracy and inability to make conceptual matches.

Phonetic searching is a commonly used approach to speech analytics

because it emphasizes the way things sound rather than attempting a

speech-to-text translation. However, because this method treats words solely

as combinations of sound with no awareness of their context, it cannot

differentiate between words and phrases that sound similar but have different

conceptual meanings. As a result, this method frequently returns high levels

offalsepositives.Forexample,thesentence“Thecomputercanrecognize

speech”containsthesamebasicphonemecomponentsas“Theoilspillwill

wreckanicebeach,”whilethemeaningisentirelydifferent.Aphonetic-based

speechenginewouldnotbeabletotellthedifference.Inaddition,phonetic

searchingoftencannotrecognizewhenabasephonemeisactuallyapartofalarger,morecomplexword,suchas“cat”in

theword“catastrophe”or“category”.Phoneticsearchingmethodologybecomesextremelyweakwhenthesearchinvolves

very short words that contain only one or two syllables due to the vast numbers of potential matches.

Word Spotting Word spotting is the process of recognizing isolated words by matching them to the sounds that are produced. As with

phoneme matching, word spotting techniques search for words out of context, so they are unable to differentiate between

words that sound alike but have completely different meanings. Because the system relies on exact sound matches, it is

also unable to account for changes in pronunciation that affect sound, such as accents or plurals.

Traditional approaches like phoneme processing and word spotting cannot account for multiple expressions of the same

concept,suchasthewords“supervisor”and“manager”havingthesameconceptualmeaningwithinacertaincontext.In

this case, any information that is related to the search term but does not contain the same phonemes will not be retrieved,

limiting the user to only a handful of relevant information. Because these methods cannot make conceptual associations,

they often miss related information that is not included in the search terms.

Most of the competition uses Phonetics to process speech. With this method, phonetics looks for sounds irrespective of the words, they do not try and determine the meaning of the words.

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ConceptualUnderstandingDuetothevariablesinspeechandlanguage,legacyapproacheslikephoneticsearchingandwordspottingalonearenot

enough to determine what is truly being said. While Autonomy supports phonetic and word-spotting methods for search

and retrieval, it also delivers sophisticated audio recognition and analysis technology that allows end-users to search audio

data from a number of sources, and further narrow results by topic, speaker, and level of emotion present in the recording

or interaction. This solution supports both keyword searches and natural language queries to retrieve audio content within

the enterprise.

Because Autonomy’s technology understands the meaning of information, it delivers the ability to search the content of

audio and video assets and does not rely on tagging or metadata to return accurate results. By automatically forming a

conceptual understanding of speech information, Autonomy speech analytics delivers automatic and accurate retrieval

of files containing audio without human intervention or manual definition of search terms, making it the market's most

advanced form of speech analytics.

ConceptualunderstandingfurtherenablesAutonomy'sIntelligentData

OperatingLayer(IDOL)toautomaticallycategorizeandanalyzeaudio

information based on its meaning to deliver advanced functionality

such as clustering, trend analysis, and emotion detection.

Query: “Madonna”

Query Search

Results: Documents Containing “Madonna” Conceptual Clustering

Documents about:1. Singer2. Italian Renaissance3. Religious Icon

Most Likely Meaning...

ResultDocuments

FurtherSuggestions...

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LanguageIndependentVoiceAnalysisAutonomy’s speech technology is language independent; it does not rely on vocabulary and grammatical rules, but derives

understanding based purely on context. This allows the solution to develop a human-like understanding of the concepts

spoken rather than by connecting specific sounds to specific words or meanings. With this functionality, Autonomy’s

technology can determine meaning no matter what language is spoken, enabling both cross-lingual and multi-lingual

analysis of audio information.

Inaddition,Autonomy’sspeechanalyticstoolintelligentlyrecognizesaccentsandlanguagesandautomaticallyshifts

the language model to the appropriate language in real-time. This is especially critical for companies that operate in

global markets with multiple languages and dialects being served. Because the language model is self-learning, it can

automatically add new terminology in any language to its vocabulary based on the context of the words being spoken.

Autonomysupportsspeechrecognitionandanalysisinmorethan20languages,includingEnglish,Spanish,Danish,

French,German,Hungarian,Italian,Polish,Portuguese,Romanian,Russian,andSimplifiedChinese.

Advanced AnalyticsAutonomy delivers advanced analytic capabilities that extend far beyond keyword search functionality to uncover actionable

information embedded in enterprise speech and audio assets, such as contact center interactions. Autonomy’s core

technology,theIntelligentDataOperatingLayer(IDOL)automaticallyprocessesaudioandvideodataandexposesthis

intelligence to the entire enterprise through keyword and natural language search functionality, trend identification, cluster

mapping, and other forms of advanced analysis.

UsingIDOLasthefoundationforenterprisespeechanalytics,userscanfindmatchestotypedandspokenqueriesbased

on the main concepts and ideas that are present in data types with embedded audio information, even if different words

andphrasesareusedtodescribethesameconcepts.IDOL’sconceptualsearchfunctionalityadditionallygroupsdatawith

related meanings, automating many complex enterprise processes and simplifying information management.

Cluster Mapping Trend Analysis

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AutomaticQueryGuidanceAutomaticQueryGuidance(AQG)dynamicallyclustersresultsintorelevantgroupswhenasearchisperformed,suggesting

further topics or information that are related to the initial query. Suggestions are provided automatically and in real-time to

intelligently assist the end-user in navigating large amounts of data. Unlike other approaches, the Autonomy solution does

not rely on intensive and subjective manual tagging in order to provide relevant information to the user.

Hot and Breaking Topics One of the greatest challenges businesses face is the identification of emerging trends, such as customer behavior,

operationalissues,orcompetitiveinformation.IDOL’s‘HotandBreaking’featureautomaticallypresentsnewand

commontopicsastheyarediscussedwithouttheend-userhavingtoperformasearch.‘Hot’resultsrepresenttopicsfrom

interactionsthatarehighinvolume,while‘Breaking’resultsareidentifiedbyIDOLasnew.Thissolutionalsoenablesthe

user to compare hot and breaking information to previously identified trends.

ClusteringClusteringisauniquefeaturethatpartitionsinformationsothatdatawithsimilartopicsorconceptsautomatically“clusters”

together without definition. This information is displayed in a two dimensional map, which allows the user to visualize the

commonthemesthatexistbetweeninteractions.Resultsarerankedbytheirconceptualsimilarity,whichisessentialto

retrieving interactions most relevant to a query, even if they contain different key words.

Script Adherence Script adherence functionality enables contact center, business, and compliance managers to automatically monitor voice

interactions for a number of purposes. The application will compare any interaction – whether it is conducted through

voice, email, or chat – to a model script and immediately alert managers to any significant deviation, enabling the

immediate resolution of issues related to legal compliance, risk, fraud, or performance.

Trend Analysis Trend analysis is crucial to identifying and responding to client, product, or operational issues that are discussed. By

automatically grouping interactions with similar concepts, speech analytics can uncover emerging issues and automatically

alert the business. This feature also identifies customer, market, and competitive trends over a specific amount of time,

delivering timely information to departments such as sales, marketing, development, and customer service.

Sentiment AnalysisSentiment analysis consists of speaker separation and the identification of heightened emotion and cross talk within an

interaction, providing great detail to the business about the identity and emotional state of clients or customers. This

feature works by displaying each speaker and areas of cross-talk in different colors in the media player when an interaction

is played back. End-users can additionally search for interactions containing heightened emotion or filter a keyword search

by whether they contain a certain degree of emotion.

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Sentiment analysis is highly valuable to the business, as it aids in the understanding of customer attitudes, behaviors,

expectations,andintentions.Italsoprovidesrootcauseanalysisofinteractionsinwhichacustomerwasupset,angry,or

confused, providing additional content for training and development.

Multi-ChannelInteractionAnalysisInadditiontospeechinformation,IDOLtechnologycanbeappliedtootherelectronicformsofcommunicationsuchas

chatandemail.ChatandemailinteractionsareingestedintoIDOLandareanalyzedandsearchedinthesamemanner

asvoiceinteractions.BecauseIDOLisaninfrastructureplatform,voice,email,andchatareprocessedinasinglesolution,

enabling the business to obtain relevant intelligence from all forms of interactions.

About AutonomyAutonomyCorporationplc(LSE:AU.orAU.L),agloballeaderininfrastructuresoftwarefortheenterprise,spearheadsthe

MeaningBasedComputingmovement.ItwasrecentlyrankedbyIDCastheclearleaderinenterprisesearchrevenues,

with market share nearly double that of its nearest competitor. Autonomy's technology allows computers to harness the

full richness of human information, forming a conceptual and contextual understanding of any piece of electronic data,

including unstructured information, such as text, email, web pages, voice, or video. Autonomy's software powers the

full spectrum of mission-critical enterprise applications including pan-enterprise search, customer interaction solutions,

informationgovernance,end-to-endeDiscovery,recordsmanagement,archiving,businessprocessmanagement,web

content management, web optimization, rich media management and video and audio analysis.

Autonomy's customer base is comprised of more than 20,000 global companies, law firms and federal agencies including:

AOL,BAESystems,BBC,Bloomberg,Boeing,Citigroup,CocaCola,DaimlerAG,DeutscheBank,DLAPiper,Ericsson,

FedEx,Ford,GlaxoSmithKline,LloydsTSB,NASA,Nestlé,theNewYorkStockExchange,Reuters,Shell,Tesco,T-Mobile,

theU.S.DepartmentofEnergy,theU.S.DepartmentofHomelandSecurityandtheU.S.SecuritiesandExchange

Commission.Morethan400companiesOEMAutonomytechnology,includingSymantec,Citrix,HP,Novell,Oracle,

SybaseandTIBCO.

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The information contained in this document represents the current opinion as of the date of publication of Autonomy Systems Ltd. regard-ing the issues discussed. Autonomy's opinion is based upon our review of competitor product information publicly available as of the date

of this document.

Because Autonomy must respond to changing market conditions, it should not be interpreted to be commitment on the part of Autonomy, and Autonomy cannot attest to the accuracy of any information presented after the date of publication.

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(Autonomy Inc. and Autonomy Systems Limited are both subsidiaries of Autonomy Corporation plc.)