Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs

2026-07-07 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

Lychee-FD is a new end-to-end full-duplex framework designed to address severe modality interference in Spoken Language Models (SLMs). This interference, identified as inherent gradient conflicts between acoustic and semantic modeling when modalities share deep parameter space, causes substantial knowledge degradation and compromises semantic integrity. Lychee-FD mitigates this by proposing a hierarchical parameter separation strategy that decouples conflicting modalities in deep layers while preserving cross-modality coherence via a dedicated semantic alignment channel. Experiments on multiple full-duplex benchmarks demonstrate Lychee-FD significantly improves speech intelligence by +7.4% on Spoken QA and full-duplex interaction fluidity by +28.5% on FullDuplexBench 1.5, without compromising inference efficiency. This work is the first to uncover the root cause of modality interference and provide a practical solution for high-performance, native intelligent full-duplex SLMs.

Key takeaway

For NLP Engineers developing full-duplex Spoken Language Models, Lychee-FD's approach offers a critical solution to modality interference. You should consider implementing hierarchical parameter separation and a dedicated semantic alignment channel in your SLM architectures. This strategy can significantly enhance speech intelligence and interaction fluidity, moving your models closer to natural, intelligent conversational agents without sacrificing inference efficiency.

Key insights

Hierarchical parameter separation resolves modality interference in full-duplex SLMs, improving intelligence and fluidity.

Principles

• Modality interference stems from inherent gradient conflicts.
• Decouple conflicting modalities in deep layers.
• Preserve cross-modality coherence via dedicated alignment.

Method

Lychee-FD uses hierarchical parameter separation to decouple acoustic and semantic modalities in deep layers, maintaining coherence via a semantic alignment channel.

In practice

• Design SLMs with separated acoustic-semantic parameters.
• Implement dedicated semantic alignment channels.

Topics

• Spoken Language Models
• Full-Duplex SLMs
• Modality Separation
• Semantic Coherence
• Hierarchical Architectures
• Gradient Conflicts

Best for: Research Scientist, AI Engineer, AI Scientist, NLP Engineer, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.