Parameter-Efficient Transfer Learning under Federated Learning for Automatic Speech Recognition

Autor:	Kan, Xuan, Xiao, Yonghui, Yang, Tien-Ju, Chen, Nanxin, Mathews, Rajiv
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Cryptography and Security Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This work explores the challenge of enhancing Automatic Speech Recognition (ASR) model performance across various user-specific domains while preserving user data privacy. We employ federated learning and parameter-efficient domain adaptation methods to solve the (1) massive data requirement of ASR models from user-specific scenarios and (2) the substantial communication cost between servers and clients during federated learning. We demonstrate that when equipped with proper adapters, ASR models under federated tuning can achieve similar performance compared with centralized tuning ones, thus providing a potential direction for future privacy-preserved ASR services. Besides, we investigate the efficiency of different adapters and adapter incorporation strategies under the federated learning setting.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.11873 Zobrazit plný text záznamu View this record from Arxiv