A Framework for Mining Speech-to-Text Transcripts of the Customer for Automated Problem Remediation
Prateeti Mohapatra, Gargi Dasgupta
Abstract
Technical support services get several thousand voice calls every year. These calls vary across a range of technical issues or maintenance requests for a suite of hardware and software products. On receiving the call, a support agent creates a ser- vice request artifact that contains her interpretation of the customer’s problem. This service request goes through the life cycle of the problem remediation process with the resolution also being recorded as part of the service request. It has been empirically observed that the actual complaint voiced by the customer is often different from the recorded interpretation in the service request. The service request created by sup- port agents runs the risk of missing key information elements present in the customer voice records. In this paper, we build a framework that taps into voice calls and uses unsupervised and supervised learning methods to enrich the service requests with additional information. The enriched data is then used for automated problem resolution.
BibTeX
@article{Mohapatra_Dasgupta_2024, title={A Framework for Mining Speech-to-Text Transcripts of the Customer for Automated Problem Remediation}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/30333}, DOI={10.1609/aaai.v38i21.30333}, abstractNote={Technical support services get several thousand voice calls
every year. These calls vary across a range of technical issues
or maintenance requests for a suite of hardware and software
products. On receiving the call, a support agent creates a ser-
vice request artifact that contains her interpretation of the
customer’s problem. This service request goes through the life
cycle of the problem remediation process with the resolution
also being recorded as part of the service request. It has been
empirically observed that the actual complaint voiced by the
customer is often different from the recorded interpretation
in the service request. The service request created by sup-
port agents runs the risk of missing key information elements
present in the customer voice records. In this paper, we build
a framework that taps into voice calls and uses unsupervised
and supervised learning methods to enrich the service requests
with additional information. The enriched data is then used
for automated problem resolution.}, number={21}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Mohapatra, Prateeti and Dasgupta, Gargi}, year={2024}, month={Mar.}, pages={22941-22947} }