FL-former: Chinese automatic speech recognition architecture under the federated large model
Year: 2024
Type: conference-paper
Abstract: In the context of the rapid development of automatic speech recognition (ASR) technology, improving model accuracy while protecting data privacy has become an important research topic. This paper proposes a new federated large model framework to improve the accuracy of ASR while effectively solving industry privacy issues. It leverages each customer's unique data sources for individual training, shares parameters for trained models, and enhances the ability to extract features from heterogeneous sources. In addition, the framework also incorporates a large language model to fully utilize its powerful semantic understanding capabilities to further improve the accuracy of speech recognition. The proposed model architecture achieves a 27.5% performance improvement compared to a single speech recognition model.
Authors: Jianfeng Chen, Zhongliang Li, Shaowei Shen, Jie Yang, Zhibin Gao, Lianfen Huang, Yifeng Zhao
Institutions: Xiamen University, Jimei University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4409965537