Speech-FT: A Fine-tuning Strategy for Enhancing Speech Representation Models Without Compromising Generalization Ability

• URL: http://arxiv.org/abs/2502.12672v1
• Date: Tue, 18 Feb 2025 09:23:42 GMT
• Title: Speech-FT: A Fine-tuning Strategy for Enhancing Speech Representation Models Without Compromising Generalization Ability
• Authors: Tzu-Quan Lin, Wei-Ping Huang, Hao Tang, Hung-yi Lee,
• Abstract summary: Speech-FT is a strategy for speech representation models that leverages model merging to preserve generalization ability while still benefiting from fine-tuning.<n> Speech-FT is effective across different fine-tuning scenarios and is compatible with various types of speech representation models.
• Score: 51.56024241398741
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Speech representation models are highly effective at extracting general features for various tasks. While fine-tuning can enhance these representations for specific applications, it often compromises their generalization ability. To address this challenge, we propose Speech-FT, a fine-tuning strategy for speech representation models that leverages model merging to preserve generalization ability while still benefiting from fine-tuning. Speech-FT is effective across different fine-tuning scenarios and is compatible with various types of speech representation models, providing a versatile solution. Speech-FT offers an efficient and practical approach to further improving general speech representations after pre-training.

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