24:, it is known for analyzing the topic being discussed, which is weighed against the emotional character of the speech and the amount and locations of speech versus non-speech during the interaction. Speech analytics in contact centers can be used to mine recorded customer interactions to surface the intelligence essential for building effective cost containment and customer service strategies. The technology can pinpoint cost drivers, trend analysis, identify strengths and weaknesses with processes and products, and help understand how the marketplace perceives offerings.
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are only few tens of unique phonemes in most languages, and the output of this recognition is a stream (text) of phonemes, which can then be searched. Large-vocabulary continuous speech recognition (LVCSR, more commonly known as speech-to-text, full transcription or ASR - automatic speech recognition) uses a set of words (bi-grams, tri-grams etc.) as the basic unit. This approach requires hundreds of thousands of words to match the audio against. It can surface new business issues, the queries are much faster, and the accuracy is higher than the phonetic approach.
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recognition unit, rather than words, comparisons using this measure cannot be made. When speech analytics systems are used to search for spoken words or phrases, what matters to the user is the accuracy of the search results that are returned. Because the impact of individual recognition errors on these search results can vary greatly, measures such as word error rate are not always helpful in determining overall search accuracy from the user perspective.
60:, are typical ways of quantifying the response of a speech analytics search system. Precision measures the proportion of search results that are relevant to the query. Recall measures the proportion of the total number of relevant items that were returned by the search results. Where a standardised test set has been used, measures such as precision and recall can be used to directly compare the search performance of different speech analytics systems.
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Speech analytics provides a
Complete analysis of recorded phone conversations between a company and its customers. It provides advanced functionality and valuable intelligence from customer calls. This information can be used to discover information relating to strategy, product, process, operational
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Speech analytics vendors use the "engine" of a 3rd party and others develop proprietary engines. The technology mainly uses three approaches. The phonetic approach is the fastest for processing, mostly because the size of the grammar is very small, with a phoneme as the basic recognition unit. There
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is the process of analyzing recorded calls to gather customer information to improve communication and future interaction. The process is primarily used by customer contact centers to extract information buried in client interactions with an enterprise. Although speech analytics includes elements of
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The process can isolate the words and phrases used most frequently within a given time period, as well as indicate whether usage is trending up or down. This information is useful for supervisors, analysts, and others in an organization to spot changes in consumer behavior and take action to reduce
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and prediction is based on three main classifiers: kNN, C4.5 and SVM RBF Kernel. This set achieves better performance than each basic classifier taken separately. It is compared with two other sets of classifiers: one-against-all (OAA) multiclass SVM with Hybrid kernels and the set of classifiers
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According to the US Government
Accountability Office, “data reliability refers to the accuracy and completeness of computer-processed data, given the uses they are intended for.” In the realm of Speech Recognition and Analytics, “completeness” is measured by the “detection rate”, and usually as
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Making a meaningful comparison of the accuracy of different speech analytics systems can be difficult. The output of LVCSR systems can be scored against reference word-level transcriptions to produce a value for the word error rate (WER), but because phonetic systems use phones as the basic
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issues and contact center agent performance. In addition, speech analytics can automatically identify areas in which contact center agents may need additional training or coaching, and can automatically monitor the customer service provided on calls.
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having the largest market share. The growth rate is attributed to rising requirements for compliance and risk management as well as an increase in industry competition through market intelligence. The
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Speech analytics applications can spot spoken keywords or phrases, either as real-time alerts on live audio or as a post-processing step on recorded speech. This technique is also known as
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call volumes—and increase customer satisfaction. It allows insight into a customer's thought process, which in turn creates an opportunity for companies to make adjustments.
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which consists of the following two basic classifiers: C5.0 and Neural
Network. The proposed variant achieves better performance than the other two sets of classifiers.
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segments of the industry are considered to hold the largest market share with expected growth from the travel and hospitality segments.
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Market research indicates that speech analytics is projected to become a billion dollar industry by 2020 with
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Scientific and
Technical Journal of Information Technologies, Mechanics and Optics
171:"Speech analytics: Why the big data source isn't music to your competitors' ears"
152:. Destination CRM (Destination: Customer Relationship Management). pp. 32–33
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248:
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398:"Speech Analytics Industry Market Share, Size, Growth & Forecast 2025"
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315:"What Does Speech Analytics Software Actually Do? - KnowledgeSpace"
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193:"Top five benefits of speech analytics for the call center"
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384:"Speech Analytics Market Worth 1.60 Billion USD by 2020"
333:"The Right Technology for your Speech Analytics Project"
249:"Reverse a Pattern of Poor Sales With Speech Analytics"
298:"Assessing the Reliability of Computer-Processed Data"
359:"Extended speech emotion recognition and prediction"
235:"Do Speech Analytics Tools Change Agent Behavior?"
68:accuracy goes up, the detection rate goes down.
263:"The Age of Speech Analytics Is Close at Hand"
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283:C. D. Manning, P. Raghavan, and H. SchĂĽtze,
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303:. United States General Accounting Office.
150:"The Why Factor in Speech Analytics About"
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357:S.E. Khoruzhnikov; et al. (2014).
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460:Computational auditory scene analysis
285:Introduction to Information Retrieval
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533:Customer relationship management
56:, commonly used in the field of
173:. Tech Republic. 8 January 2016
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207:"Speech & Text Analytics"
148:Coreen Bailor (August 2006).
22:automatic speech recognition
465:Music information retrieval
221:"Real Time Voice Analytics"
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82:speech emotion recognition
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507:3D sound reconstruction
502:3D sound localization
119:Customer intelligence
58:Information retrieval
450:Acoustic fingerprint
54:Precision and recall
485:Speaker recognition
528:Speech recognition
490:Speech recognition
129:Speech recognition
99:telecommunications
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497:Sound recognition
475:Speech processing
439:Computer audition
265:. Destination CRM
124:Customer dynamics
52:Measures such as
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338:. CallMiner
107:outsourcing
522:Categories
209:. Genesys.
156:2013-10-30
135:References
72:Technology
28:Definition
400:. MENAFN.
369:(6): 137.
223:. Xdroid.
80:Extended
41:Usability
113:See also
237:. ICMI.
89:Growth
336:(PDF)
301:(PDF)
344:2016
271:2016
179:2016
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