EmoTransCap: Dataset and Pipeline for Emotion Transition-Aware Speech Captioning in Discourses

• URL: http://arxiv.org/abs/2604.26417v1
• Date: Wed, 29 Apr 2026 08:27:39 GMT
• Title: EmoTransCap: Dataset and Pipeline for Emotion Transition-Aware Speech Captioning in Discourses
• Abstract summary: We propose Emotion Transition-Aware Speech Captioning (EmoTransCap), a paradigm that integrates temporal emotion dynamics with discourse-level speech description.<n>This is the first large-scale dataset explicitly designed to capture discourse-level emotion transitions.<n>We also introduce a controllable, transition-aware emotional speech synthesis system at the discourse level.
• Score: 25.739767606548313
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Emotion perception and adaptive expression are fundamental capabilities in human-agent interaction. While recent advances in speech emotion captioning (SEC) have improved fine-grained emotional modeling, existing systems remain limited to static, single-emotion characterization within isolated sentences, neglecting dynamic emotional transitions at the discourse level. To address this gap, we propose Emotion Transition-Aware Speech Captioning (EmoTransCap), a paradigm that integrates temporal emotion dynamics with discourse-level speech description. To construct a dataset rich in emotion transitions while enabling scalable expansion, we design an automated pipeline for dataset creation. This is the first large-scale dataset explicitly designed to capture discourse-level emotion transitions. To generate semantically rich descriptions, we incorporate acoustic attributes and temporal cues from discourse-level speech. Our Multi-Task Emotion Transition Recognition (MTETR) model performs joint emotion transition detection and diarization. Leveraging the semantic analysis capabilities of LLMs, we produce two annotation versions: descriptive and instruction-oriented. These data and annotations offer a valuable resource for advancing emotion perception and emotional expressiveness. The dataset enables speech captions that capture emotional transitions, facilitating temporal-dynamic and fine-grained emotion understanding. We also introduce a controllable, transition-aware emotional speech synthesis system at the discourse level, enhancing anthropomorphic emotional expressiveness and supporting emotionally intelligent conversational agents.

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