Beyond Semantic Dominance: Cognitive Affective Reasoning and Empathetic Response Alignment in Audio Language Models
Abstract
While Audio Language Models (ALMs) demonstrate strong semantic understanding, they struggle with complex affective interactions. Specifically, textual semantic dominance often overshadows acoustic nuances, and a lack of cognitive depth leads to generic, emotion-agnostic responses. We propose CogAudio-LLM\footnote{ \urlstyle{same} https://github.com/zxzhao0/CogAudio-LLM, a novel cognitive affective reasoning framework. To mitigate semantic dominance, we build LIME-440K, a ``lexically-identical, multi-emotion'' dataset designed to facilitate acoustic-semantic decoupling. We introduce EIPS, a 4-step Chain-of-Thought (CoT) mechanism incorporating psychological reasoning. For inference efficiency, multi-stage training explicitly establishes EIPS via supervised fine-tuning, then distills this logic into an implicit generation process. Finally, we design DR-SAPO (Dual-Route Soft Adaptive Policy Optimization) to dynamically balance the logical rigor of the CoT with the empathetic quality of the direct response.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents CogAudio-LLM, a framework that significantly enhances the emotional insight and empathetic response capabilities of Audio Language Models through a novel cognitive affective reasoning approach. This work is a meaningful contribution to the field, addressing critical challenges in multimodal emotional understanding and response generation.
Methodology
The proposed methodology is robust, introducing a novel framework (CogAudio-LLM) that addresses the limitations of existing Audio Language Models (ALMs) by integrating cognitive affective reasoning through a structured Chain-of-Thought (CoT) mechanism. The construction of the LIME-440K dataset is particularly noteworthy, as it effectively facilitates the decoupling of semantic and acoustic information, which is a critical innovation in the field. The multi-stage training approach, which includes explicit reasoning followed by implicit internalization, is well-conceived and enhances the model's ability to generate empathetic responses. The introduction of the DR-SAPO algorithm for reinforcement learning adds a significant layer of sophistication to the training process, allowing for dynamic balancing of logical reasoning and empathetic response generation.
Results & evaluation
The experimental setup is comprehensive, utilizing a large-scale dataset (LIME-440K) and evaluating the model against multiple benchmarks, including real human utterances. The results demonstrate significant improvements in both emotion recognition accuracy and empathetic response quality, particularly in challenging scenarios where semantic and acoustic cues conflict. The use of both objective and subjective metrics for evaluation, including human annotations, strengthens the validity of the findings. The ablation studies further clarify the contributions of various components of the model, providing insight into the effectiveness of the proposed methods.
Reproducibility
The paper provides sufficient implementation details, including model initialization, training parameters, and evaluation metrics, which should facilitate reproducibility. However, the reliance on specific models for data generation and evaluation (e.g., Gemini2.5-pro) may introduce dependencies that could complicate full replication without access to these resources.
Limitations
One limitation noted is the potential gap between the training data and real-world spontaneous speech, particularly regarding micro-prosody, which may affect the model's performance in practical applications. Additionally, while the model shows promise, its performance in highly nuanced emotional contexts beyond the tested scenarios remains to be fully explored.
Broader impact
The implications of this research are significant, as it advances the capabilities of ALMs in understanding and responding to human emotions, which is crucial for applications in empathetic AI, virtual assistants, and mental health support systems. By addressing the semantic dominance issue, the framework could lead to more contextually aware and emotionally intelligent systems, enhancing human-computer interaction. The paper presents CogAudio-LLM, a framework that significantly enhances the emotional insight and empathetic response capabilities of Audio Language Models through a novel cognitive affective reasoning approach. This work is a meaningful contribution to the field, addressing critical challenges in multimodal emotional understanding and response generation.
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