SCoPE: Shift-Aware Speaker-Conditioned Priors for Emotion Recognition in Conversations

2026-07-25 · Source: cs.CL updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, extended

Summary

SCoPE (Shift-Aware Speaker-Conditioned Priors for Emotion Recognition) is a lightweight module designed to enhance Emotion Recognition in Conversations (ERC) by explicitly modeling speaker-specific emotional history and predicting emotion shifts. It addresses limitations of existing multimodal ERC models that struggle with noisy signals and implicit temporal dynamics. SCoPE integrates a GRU-based module to generate speaker-conditioned priors, an emotion shift prediction head, and a shift-aware fusion mechanism. This dynamic fusion balances historical priors with current multimodal evidence, prioritizing evidence when shifts are likely. The model achieved superior performance on the IEMOCAP dataset, with 75.72% Accuracy and 75.82% Weighted F1, outperforming recent state-of-the-art models. It also showed competitive results on the MELD dataset, particularly benefiting rare emotion labels like Fear and Disgust. SCoPE requires only 3.8 GB of GPU memory and boasts low inference latency (0.20-0.25 ms per utterance), making it suitable for real-time applications.

Key takeaway

For Machine Learning Engineers developing real-time human-robot interaction or affective computing systems, SCoPE offers a computationally efficient and accurate approach to multimodal emotion recognition. You should consider integrating speaker-conditioned priors and dynamic shift-aware fusion to improve model robustness against noisy signals and enhance performance, especially for rare emotion labels. This lightweight architecture, requiring only 3.8 GB VRAM and sub-millisecond inference, is ideal for deployment on consumer-level GPUs.

Key insights

SCoPE dynamically fuses speaker-conditioned emotional history with multimodal evidence, guided by predicted emotion shifts, for robust ERC.

Principles

• Emotions persist across utterances but are speaker-specific.
• Emotion shifts are often context-driven and predictable.
• Dynamically balancing prior and evidence improves ERC accuracy.

Method

SCoPE uses a GRU to generate speaker-conditioned priors from past posteriors and speaker identity. An emotion shift head predicts shifts, guiding a precision-weighted logit integration between priors and multimodal evidence.

In practice

• Integrate GRU-based modules for temporal emotion memory.
• Use emotion shift prediction as a dynamic control signal.
• Apply Bayesian-inspired fusion for multimodal ERC.

Topics

• Emotion Recognition in Conversations
• Multimodal Emotion Recognition
• Speaker-Conditioned Priors
• Emotion Shift Prediction
• Dynamic Fusion
• Human-Robot Interaction

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.