Business In telligence and Customer Insight in the Enterprise are improving fast b ecause of two important phenomena:
\n\nThe techniques for natural language processing, advanced analy tics, and machine learning are becoming almost a commodity through the hard work of companies like Amazon, Google, and the Data Science Community.&nbs p;
\n \nMore and more data in the enterprise has become available for analytics.
\n \n\nWe now routinely take unstructured text in the Enterprise and use natural language processing (NLP) techniques to extract values and features from written natural language. But within the area of unstructured information, often termed “Dark Data," there are still opp ortunities where we can expect a lot of gain. One key area of Dark Data dev elopment is in the domain of spoken interactions between company representa tives and customers.
\nCurrently, companies have few options ot her than to record conversations, much less perform analytics on the conten t of these calls. Yes, IBM, Google, and Amazon offer speech recognition tha t theoretically could be used to turn voice into text and then use NLP tech niques, but the problem is that these systems are not able to be “tra ined” on Enterprise-specific taxonomies. The accuracy and value to th e Enterprise is contingent on these systems being able to manage industry-s pecific lingo, product names, and acronyms, to name a few requirements.&nbs p;
\nDuring this presentation, we will demonstrate an approach for ta xonomy-driven speech recognition so that industry-specific spoken text can be adequately recognized for NLP processing and subsequent use in a custome r Knowledge Graph.
\n DTSTART:20200325T104500 SUMMARY:Conquering the Last Vestiges of Dark Data – Speech Recognition DTEND:20200325T114459 LOCATION: See Description END:VEVENT END:VCALENDAR