Enriching Speech Emotion Representations with Conversational Context

2026-09-22

AI Summary

本文通过引入ACERT模块，利用对话上下文改进语音情感识别，解决传统方法忽视对话情境的问题，在多个数据集上表现优异。

Abstract

Detecting emotions is necessary for building systems that can accurately and adaptively interact with humans. Speech Emotion Recognition (SER) has become an important research focus to develop intelligent spoken interfaces. However, most studies predict emotions at the utterance level, ignoring the conversational context, along with the emotional flow and speaker interactions it carries. In this paper, we introduce ACERT (Averaged Contextual Emotion Representation through Time), a module that integrates a flexible-length window of conversational context to better capture emotional evolution in spoken interactions. To evaluate the robustness of this method, we conducted experiments on datasets spanning diverse emotionally expressive styles and contexts. ACERT outperforms current state-of-the-art (SOTA) approaches on IEMOCAP, establishes the first context-aware benchmark on SAFE, and obtains strong results on MELD for unweighted, class-balanced metrics. Ablation studies show that ACERT's gains come from emotional and conversational continuity, rather than from speaker identity or acoustic conditions.

Problem

• Speech Emotion Recognition
• conversational context
• emotional flow
• speaker interactions

Innovation

• ACERT
• conversational context
• emotional evolution
• speech emotion recognition