ES4R: Speech Encoding Based on Prepositive Affective Modeling for Empathetic Response Generation
Abstract
Empathetic speech dialogue requires not only understanding linguistic content but also perceiving rich paralinguistic information such as prosody, tone, and emotional intensity for affective understandings. Existing speech-to-speech large language models either rely on ASR transcription or use encoders to extract latent representations, often weakening affective information and contextual coherence in multi-turn dialogues. To address this, we propose \textbf{ES4R}, a framework for speech-based empathetic response generation. Our core innovation lies in explicitly modeling structured affective context before speech encoding, rather than relying on implicit learning by the encoder or explicit emotion supervision. Specifically, we introduce a dual-level attention mechanism to capture turn-level affective states and dialogue-level affective dynamics. The resulting affective representations are then integrated with textual semantics through speech-guided cross-modal attention to generate empathetic responses. For speech output, we employ energy-based strategy selection and style fusion to achieve empathetic speech synthesis. ES4R consistently outperforms strong baselines in both automatic and human evaluations and remains robust across different LLM backbones.
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 ES4R, a novel framework for empathetic response generation that effectively models structured affective context from speech inputs, significantly advancing the field of empathetic dialogue systems.
Methodology
The proposed ES4R framework introduces a dual-level attention mechanism for affective context modeling, which is a significant advancement over existing methods that rely on implicit learning or explicit emotion supervision. By explicitly modeling affective states and dynamics before encoding, the methodology addresses key limitations in current empathetic response generation systems. The integration of speech-guided cross-modal attention further enhances the model's ability to generate contextually relevant and emotionally resonant responses.
Results & evaluation
The experiments conducted on the AvaMERG dataset demonstrate the effectiveness of ES4R, with consistent improvements over strong baselines in both automatic and human evaluations. The inclusion of ablation studies provides insights into the contributions of different components, validating the importance of the dual-level attention and cross-modal fusion mechanisms. The results indicate robust performance across various LLM backbones, showcasing the framework's versatility.
Reproducibility
The paper provides detailed descriptions of the architecture, training strategies, and evaluation metrics used, which supports reproducibility. However, the absence of a publicly available implementation or code repository limits the ability for others to replicate the results fully.
Limitations
While the framework shows promise, it relies on a relatively simple set of empathetic speaking styles and does not incorporate additional paralinguistic cues, which could enhance the naturalness of the generated speech. Future work is needed to explore more nuanced emotional expressions and the incorporation of speaker-aware personalization.
Broader impact
The development of empathetic dialogue systems has significant implications for applications in mental health support, customer service, and social robotics, where understanding and responding to human emotions is crucial. The ES4R framework could enhance user experience in these domains by providing more emotionally aware interactions. The paper presents ES4R, a novel framework for empathetic response generation that effectively models structured affective context from speech inputs, significantly advancing the field of empathetic dialogue systems.
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