Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
Developing seamless, high-performance, native intelligent full-duplex Spoken Language Models (SLMs) remains a critical challenge and long-standing goal for the speech and NLP community. Despite notable progress, recent endeavors are fundamentally constrained by severe modality interference, which causes substantial knowledge degradation and compromises semantic integrity -- ultimately making full-duplex SLMs feel unnatural and unintelligent. In this paper, through an exhaustive fine-grained analysis of model optimization dynamics, we uncover the root cause of such performance degradation, reve
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- PossiblePossibly related (embedding) · 48%huggingface/speech-to-speech →
- PossiblePossibly related (embedding) · 46%Atomic-man007/Awesome_Multimodel_LLM →
- LinkedLinked via arxiv author · 85%Zhenyu Liu →
“Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs”
- LinkedLinked via arxiv author · 85%Yunxin Li →
“Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs”
- LinkedLinked via arxiv author · 85%Xuanyu Zhang →
“Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs”
- LinkedLinked via arxiv author · 85%Qixun Teng →
“Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs”
- LinkedLinked via arxiv author · 85%Shenyuan Jiang →
“Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs”
- LinkedLinked via arxiv author · 85%Haolan Chen →
“Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs”