The Development of a Speech Recognition and Semantic Understanding System for Marketing Customer Service
The power marketing service hotline is a critical link in the power marketing service system, an essential channel for power grid companies to serve the ‘ last mile ‘ of power customers, and a necessary means for enterprises to serve customers, collect and exchange information. With the growth of users, the number of customers calling the customer service center has surged. The customer service hotline has shortcomings such as inaccurate service quality detection, data distortion of incoming calls, and lack of customer demand. As the main entrance of enterprise user and service information, call center adopts traditional methods of data collection, collation, and analysis, which are inefficient and costly. It cannot accurately extract adequate information from massive amounts of information. Limited by traditional data collection, data collection mainly adopts next-door sampling, which cannot achieve extensive data screening and accurate analysis. This paper proposes developing a marketing customer service speech recognition and semantic understanding system. By understanding the development technology of marketing customer service speech recognition and semantic understanding system, the composition of each part of the system is designed, and the content of each part is intended. The marketing customer service speech recognition and semantic understanding system has been successfully developed to achieve extensive data screening and accurate analysis of customer information. It is of great value for promotion and application.
Authors
- Huiyan Cao
- Lu Han (ORCID: https://orcid.org/0000-0002-1120-3220)
- Shuyi Tao
- Jia‐Lin Li (ORCID: https://orcid.org/0000-0002-0409-4097)
- Jiaying Wang (ORCID: https://orcid.org/0000-0002-1509-0075)
- Yuenan Wang (ORCID: https://orcid.org/0009-0001-4600-2090)
Institutions
- Liaoning Shihua University (CN)
Publication Details
- Published
- 2024-08-29
- DOI
- https://doi.org/10.1109/icsece61636.2024.10729529
- Citations
- 1
- Primary Topic
- Technology and Data Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.78