Download - Autonomy Enterprise Speech Analytics
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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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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