A Framework for Mining Speech-to-Text Transcripts of the Customer for Automated Problem Remediation - Mohapatra, Prateeti; Dasgupta, Gargi (Proceedings of the AAAI Conference on Artificial Intelligence, 2024)
Technical support services get several thousand voice callsevery year. These calls vary across a range of technical issuesor maintenance requests for a suite of hardware and softwareproducts. On receiving the call, a support agent creates a ser-vice request artifact that contains her interpretation of thecustomer’s problem. This service request goes through the lifecycle of the problem remediation process with the resolutionalso being recorded as part of the service request. It has beenempirically observed that the actual complaint voiced by thecustomer is often different from the recorded interpretationin the service request. The service request created by sup-port agents runs the risk of missing key information elementspresent in the customer voice records. In this paper, we builda framework that taps into voice calls and uses unsupervisedand supervised learning methods to enrich the service requestswith additional information. The enriched data is then usedfor automated problem resolution.