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Speech-FT framework merges pre-trained and fine-tuned models for better generalization

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Researchers have developed Speech-FT, a novel two-stage fine-tuning framework designed to improve speech representation models. This method aims to enhance performance on specific tasks without sacrificing the model's ability to generalize across different tasks. Speech-FT first reduces representational drift during fine-tuning and then interpolates with the original pre-trained model to restore generalization capabilities. Experiments show significant improvements on the SUPERB benchmark, outperforming existing methods in various fine-tuning scenarios. AI

IMPACT Offers a method to improve speech model performance and generalization, potentially benefiting downstream applications in speech recognition and speaker identification.

RANK_REASON This is a research paper detailing a new framework for speech representation models.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Speech-FT framework merges pre-trained and fine-tuned models for better generalization

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This is a research paper detailing a new framework for speech representation models.
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Tzu-Quan Lin, Wei-Ping Huang, Hao Tang, Hung-yi Lee ·

    Speech-FT: Merging Pre-trained And Fine-Tuned Speech Representation Models For Cross-Task Generalization

    arXiv:2502.12672v4 Announce Type: replace Abstract: Fine-tuning speech representation models can enhance performance on specific tasks but often compromises their cross-task generalization ability. This degradation is often caused by excessive changes in the representations, maki…

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