Hierarchical Acoustic-Semantic Modeling: Modality Separation and Semantic Coherence for Full-Duplex SLMs
arXiv:2607.06540v1 Announce Type: new Abstract: 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
The paper identifies fundamental constraints in current Spoken Language Models (SLMs) and proposes a novel hierarchical acoustic-semantic modeling approach, marking a significant theoretical advancement.
This research addresses core limitations in full-duplex SLMs, potentially leading to more natural and intelligent AI interactions, crucial for widespread adoption of agentic systems.
The proposed architecture aims to overcome modality interference and knowledge degradation, fundamentally altering the development path for high-performance conversational AI.
- · AI developers
- · Speech technology companies
- · NLP researchers
- · Enterprise AI
- · Companies relying on less sophisticated SLM architectures
- · Legacy speech recognition systems
Improved performance and naturalness in conversational AI systems.
Accelerated development and deployment of sophisticated AI agents capable of more coherent and intelligent real-time interaction.
Enhanced human-computer interaction leading to new applications and shifts in how work and personal tasks are automated.
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