{"id":156233,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/msr-research-item\/automated-quality-monitoring-in-the-call-center-with-asr-and-maximum-entropy\/"},"modified":"2018-10-16T20:18:47","modified_gmt":"2018-10-17T03:18:47","slug":"automated-quality-monitoring-in-the-call-center-with-asr-and-maximum-entropy","status":"publish","type":"msr-research-item","link":"https:\/\/newed.any0.dpdns.org\/en-us\/research\/publication\/automated-quality-monitoring-in-the-call-center-with-asr-and-maximum-entropy\/","title":{"rendered":"Automated Quality Monitoring in the Call Center with ASR and Maximum Entropy"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper describes an automated system for assigning quality scores to recorded call center conversations. The system combines speech recognition, pattern matching, and maximum entropy classification to rank calls according to their measured quality. Calls at both end of the spectrum are flagged as \u201cinteresting\u201d and made available for further human monitoring. In this process, pattern matching on the ASR transcript is used to answer a set of standard quality control questions such as \u201cdid the agent use courteous words and phrases,\u201d and to generate a question-based score. This is interpolated with the probability of a call being \u201cbad,\u201d as determined by maximum entropy operating on a set of ASR-derived features such as \u201cmaximum silence length\u201d and the occurrence of selected n-gram word sequences. The system is trained on a set of calls with associated manual evaluation forms. We present precision and recall results from IBM\u2019s North American Help Desk indicating that for a given amount of listening effort, this system triples the number of bad calls that are identified, over the current policy of randomly sampling calls.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper describes an automated system for assigning quality scores to recorded call center conversations. The system combines speech recognition, pattern matching, and maximum entropy classification to rank calls according to their measured quality. Calls at both end of the spectrum are flagged as \u201cinteresting\u201d and made available for further human monitoring. In this process, [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"user_nicename","value":"gzweig"}],"msr_publishername":"","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of ICASSP","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"Proceedings of 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