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arXiv · 2506.01496

Continual Speech Learning with Fused Speech Features

Guitao Wang·Jinming Zhao·Hao Yang·Guilin Qi·Tongtong Wu·Gholamreza Haffari
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Abstract

Rapid growth in speech data demands adaptive models, as traditional static methods fail to keep pace with dynamic and diverse speech information. We introduce continuous speech learning, a new set-up targeting at bridging the adaptation gap in current speech models. We use the encoder-decoder Whisper model to standardize speech tasks into a generative format. We integrate a learnable gated-fusion layer on the top of the encoder to dynamically select task-specific features for downstream tasks. Our approach improves accuracy significantly over traditional methods in six speech processing tasks, demonstrating gains in adapting to new speech tasks without full retraining.

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Guitao Wang, Jinming Zhao, Hao Yang, Guilin Qi, Tongtong Wu, Gholamreza Haffari. 2025-06-02. Continual Speech Learning with Fused Speech Features. https://arxiv.org/abs/2506.01496

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