Year: 2025
Type: conference-paper
Abstract: Existing end-to-end speech large language models (LLMs) usually rely on large-scale annotated data for training, while data-efficient training has not been discussed in depth.We focus on two fundamental problems between speech and text: the representation space gap and sequence length inconsistency.We propose Soundwave, which utilizes an efficient training strategy and a novel architecture to address these issues.Results show that Soundwave outperforms other advanced speech LLMs in speech translation and AIR-Bench speech tasks with only a fraction of the training data.Further analysis shows that Soundwave still retains its intelligence during conversation.
Authors: Yuhao Zhang, Zhiheng Liu, Fan Bu, Ruiyu Zhang, Benyou Wang, Haizhou Li
Institution: Chinese University of Hong Kong, Shenzhen
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Award: G524075158
OpenAlex ID: W4412944935