The true key to capturing audio, video, and text based omnichannel business intelligence is large, customized vocabularies that are specific to a company's objectives and the vertical in which they operate. Yactraq's CoreTraq platform can produce and utilize vocabularies of 10s of thousands of words in a matter of days, and at a fraction of the cost of legacy solutions. Yactraq provides professional services to help your business craft the most effective vocabulary to meet your objectives and drive success.
Yactraq maintains standard vocabulary sets that are used as a basis for our CoreTraq platform, and customized vocabularies for our customers are built upon that standard set to further enhance the capabilities of OmniTraq's features.
Accurate B2B speech systems need to understand product and brand names, as well as industry and company specific business terms; this requires the creation of custom speech vocabularies unique to each customer.
At Yactraq we focus on automating the creation of custom speech vocabularies with three distinct advantages over legacy solutions: speed, scale, and cost. We can create custom speech vocabularies in a matter of days that consist of hundreds of thousands of unique words, and all at a fraction of the cost of legacy solutions.
While training the CoreTraq engine, we use standard taxonomies as well as customer specific taxonomies. Taxonomic data is mapped to related linguistic data in the form of web pages, documents and call transcripts.
Taxonomic and linguistic data is used to train CoreTraq's natural language understanding module, which then automatically builds custom vocabulary language models for CoreTraq's speech recognition module.
In its operational mode, CoreTraq uses proprietary machine learning and natural language understanding techniques to quantitatively determine the primary subject topics of any given minute of audio or video data.
Business intelligence applications often have specific requirements driven by the need to answer precise questions. Also in such cases, only limited amounts of linguistic data may be available. In such cases Yactraq also uses class based language models in combination with various other techniques.
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