Paralinguistic Emotion-Aware Validation Timing Detection in Japanese Empathetic Spoken Dialogue

2026-03-10

AI Summary

This study addresses the challenge of accurately identifying optimal moments for affective validation in Japanese empathetic dialogue solely from speech signals, without relying on textual content. To this end, we propose a purely audio-driven model that uniquely integrates paralinguistic awareness with multi-task speech emotion encoding. Our approach leverages HuBERT-based self-supervised pretraining, multi-task emotion classification, and multimodal feature fusion to effectively model nonverbal vocal cues. Experimental results on the TESC corpus demonstrate that the proposed method significantly outperforms conventional speech-based baselines, confirming that vocal signals alone are sufficient to reliably detect appropriate timing for affective validation. This work thus opens a new pathway toward text-independent empathetic human–computer interaction.

Technology Category

• Cognitive Modeling & Cognitive Systems: Affective Computing
• Natural Language Processing: Speech
• Machine Learning: Multimodal Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Emotional Validation is a psychotherapy communication technique that involves recognizing, understanding, and explicitly acknowledging another person's feelings and actions, which strengthens alliance and reduces negative affect. To maximize the emotional support provided by validation, it is crucial to deliver it with appropriate timing and frequency. This study investigates validation timing detection from the speech perspective. Leveraging both paralinguistic and emotional information, we propose a paralinguistic- and emotion-aware model for validation timing detection without relying on textual context. Specifically, we first conduct continued self-supervised training and fine-tuning on different HuBERT backbones to obtain (i) a paralinguistics-aware Self-Supervised Learning (SSL) encoder and (ii) a multi-task speech emotion classification encoder. We then fuse these encoders and further fine-tune the combined model on the downstream validation timing detection task. Experimental evaluations on the TUT Emotional Storytelling Corpus (TESC) compare multiple models, fusion mechanisms, and training strategies, and demonstrate that the proposed approach achieves significant improvements over conventional speech baselines. Our results indicate that non-linguistic speech cues, when integrated with affect-related representations, carry sufficient signal to decide when validation should be expressed, offering a speech-first pathway toward more empathetic human-robot interaction.

Problem

• validation timing detection
• paralinguistics
• speech emotion recognition
• empathetic dialogue
• non-linguistic cues

Innovation

• paralinguistic
• emotion-aware
• validation timing detection
• self-supervised learning
• speech-based empathy