Speech Analytics google
Speech analytics is the process of analyzing recorded calls to gather information, brings structure to customer interactions and exposes information buried in customer contact center interactions with an enterprise. Although it often includes elements of automatic speech recognition, where the identities of spoken words or phrases are determined, it may also include analysis of one or more of the following: the topic(s) being discussed the emotional character of the speech the amount and locations of speech versus non-speech (e.g. call hold time or periods of silence) One use of speech analytics applications is to 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 audio mining. Other uses include categorization of speech, for example in the contact center environment, to identify calls from unsatisfied customers. Speech analytics in contact centers can be used to extract critical business intelligence that would otherwise be lost. By analyzing and categorizing recorded phone conversations between companies and their customers, useful information can be discovered relating to strategy, product, process, operational issues and contact center agent performance. This information gives decision-makers insight into what customers really think about their company so that they can quickly react. 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. …

Online Analytical Processing (OLAP) google
In computing, online analytical processing, or OLAP, is an approach to answering multi-dimensional analytical (MDA) queries swiftly. OLAP is part of the broader category of business intelligence, which also encompasses relational database, report writing and data mining. Typical applications of OLAP include business reporting for sales, marketing, management reporting, business process management (BPM), budgeting and forecasting, financial reporting and similar areas, with new applications coming up, such as agriculture. The term OLAP was created as a slight modification of the traditional database term Online Transaction Processing (“OLTP”). …

Symbolic Aggregate Approximation (SAX) google
While there are literally hundreds of papers on discretizing (symbolizing, tokenizing, quantizing) time series, none of the techniques allows a distance measure that lower bounds a distance measure defined on the original time series. For this reason, the generic time series data mining approach illustrated in Table 1 is of little utility, since the approximate solution to problem created in main memory may be arbitrarily dissimilar to the true solution that would have been obtained on the original data. If, however, one had a symbolic approach that allowed lower bounding of the true distance, one could take advantage of the generic time series data mining model, and of a host of other algorithms, definitions and data structures which are only defined for discrete data, including hashing, Markov models, and suffix trees. This is exactly the contribution of this paper. We call our symbolic representation of time series SAX (Symbolic Aggregate approXimation), and define it in the next section…. …

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