New Method Improves Speech Integration with Large Language Models Using Geometric Constraints
Researchers propose Convex Gate (C-Gate), a new technique for connecting speech signals to large language models by constraining speech representations to stay within the LLM's embedding space. The method uses convex combinations of token embeddings to preserve both continuous acoustic information and compatibility with pretrained models. The approach achieves significant improvements on speech recognition tasks while revealing that geometric structure in embedding space, rather than discrete tokens, is key to effective multimodal integration.
A new preprint from arXiv presents Convex Gate (C-Gate), a speech-to-LLM interface designed to address the challenge of integrating continuous acoustic signals into frozen large language models. The method constrains each speech frame to be represented as a convex combination of token embeddings, ensuring representations remain within the LLM's input embedding manifold while preserving acoustic expressivity. Testing on automatic speech recognition and emotion recognition tasks, C-Gate achieves up to 48.7% relative improvement on LibriSpeech word error rate while maintaining competitive emotion recognition accuracy. The researchers' analysis reveals that information is carried by time-resolved trajectories through embedding space rather than discrete token identities, and causal interventions confirm both trajectory structure and alignment to the pretrained manifold are critical. The authors have released checkpoints, outputs, and analysis tools for reproducibility.
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- arXiv cs.CLCenter
Is Text All You Need? Text as a Universal Information Bottleneck for Speech LLMs
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