Work

Research on supplier center speech recognition technology based on artificial intelligence

Year: 2025

Type: article

Abstract: In response to the lagging speech recognition capabilities in supplier services, this study integrates speech recognition, speech synthesis, and semantic understanding technologies to improve speech recognition capabilities in different environments. The Mel Cepstral acoustic feature algorithm and deep convolutional neural network model are used to construct speech feature extraction algorithm models, speech recognition acoustic models, and speech training modules, which enhance speech training, processing, recognition, and application capabilities. Integrating the designed model into the company’s business platform has improved the semantic understanding ability in the power grid field and greatly enhanced the standardized management of business data. Through experiments, the speech recognition error rate of the designed solution by our research institute has been reduced to below 1%, greatly improving the speech recognition capability of the supplier center and thereby enhancing the service level of the supplier center.

Source: Measurement and Control

DOI: 10.1177/00202940251400805

Authors: Guo Weidong, Xu Yubin, Zhou Yong, Shi Zhifeng

Institutions: State Grid Corporation of China (China), Harbin Institute of Technology, State Grid Shanxi Electric Power Company (China)

Language: English

Topic: Speech Recognition and Synthesis

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,000