As a branch of sentiment analysis tasks, emotion recognition in conversation (ERC) aims to explore the hidden emotions of a speaker by analyzing the sentiments in utterance. In addition, emotion recognition in multimodal data from conversation includes the text of the utterance and its corresponding acoustic and visual data. By integrating features from various modalities, the emotion of utterance can be more accurately predicted. ERC research faces challenges in context construction, speaker dependency design, and multimodal heterogeneous feature fusion. Therefore, this review starts by defining the ERC task, developing the research work, and introducing the utilized datasets in detail. Simultaneously, we analyzed context modeling in conversations, speaker dependency, and methods for fusing multimodal information concerning existing research work for evaluation purposes. Finally, this review also explores the research, application challenges, and opportunities of ERC.

In recent years, substantial research has focused on emotion recognition using multi-stream speech representations. In existing multi-stream speech emotion recognition (SER) approaches, effectively extracting and fusing speech features is crucial. To overcome the bottleneck in SER caused by the fusion of inter-feature information, including challenges like modeling complex feature relations and the inefficiency of fusion methods, this paper proposes an SER framework based on multi-task learning, named AFEA-Net. The framework consists of a speech emotion alignment learning (SEAL), an acoustic feature excitation-and-aggregation mechanism (AFEA), and a continuity learning. First, SEAL aligns sentiment information between WavLM and Fbank features. Then, we design an acoustic feature excitation-and-aggregation mechanism to adaptively calibrate and merge the two features. Furthermore, we introduce a continuity learning strategy to explore the distinctiveness and complementarity of dual-stream features from intra- and inter-speech. Experimental results on the publicly available IEMOCAP and RAVDESS sentiment datasets show that our proposed approach outperforms state-of-the-art SER approaches. Specifically, we achieve 75.1% WA, 75.3% UAR, 76% precision, and 75.4% F1-score on IEMOCAP, and 80.3%, 80.6%, 80.8%, and 80.4% on RAVDESS, respectively.

Sentiment analysis is pivotal in advancing human–computer interaction (HCI) systems as it enables emotionally intelligent responses. While existing models show potential for HCI applications, current conversational datasets exhibit critical limitations in real-world deployment, particularly in capturing domain-specific emotional dynamics and context-sensitive behavioral patterns—constraints that hinder semantic comprehension and adaptive capabilities in task-driven HCI scenarios. To address these gaps, we present Multi-HM, the first multimodal emotion recognition dataset explicitly designed for human–machine consultation systems. It contains 2000 professionally annotated dialogues across 10 major HCI domains. Our dataset employs a five-dimensional annotation framework that systematically integrates textual, vocal, and visual modalities while simulating authentic HCI workflows to encode pragmatic behavioral cues and mission-critical emotional trajectories. Experiments demonstrate that Multi-HM-trained models achieve state-of-the-art performance in recognizing task-oriented affective states. This resource establishes a crucial foundation for developing human-centric AI systems that dynamically adapt to users’ evolving emotional needs.