Continual Speech Learning with Fused Speech Features

• URL: http://arxiv.org/abs/2506.01496v2
• Date: Tue, 03 Jun 2025 10:16:03 GMT
• Title: Continual Speech Learning with Fused Speech Features
• Authors: Guitao Wang, Jinming Zhao, Hao Yang, Guilin Qi, Tongtong Wu, Gholamreza Haffari,
• Abstract summary: We introduce continuous speech learning, a new set-up targeting at bridging the adaptation gap in current speech models.<n>We use the encoder-decoder Whisper model to standardize speech tasks into a generative format.<n>Our approach improves accuracy significantly over traditional methods in six speech processing tasks, demonstrating gains in adapting to new speech tasks without full retraining.
• Score: 49.21227244653524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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