AAAI 2026 | 革新电影配音工业流程：AI首次学会「导演-演员」配音协作模式

Core Viewpoint - The article discusses the limitations of AI voice dubbing, particularly its lack of emotional depth, and introduces a new framework called Authentic-Dubber that incorporates director-actor interaction to enhance emotional expression in AI-generated voiceovers [2][3][19]. Group 1: AI Dubbing Limitations - AI voice dubbing often lacks the "human touch," as it skips the crucial director-actor interaction that brings emotional depth to performances [2][3]. - The current AI models simplify the dubbing process by having AI "actors" read scripts without the guidance of a director, resulting in a lack of emotional resonance [2][3]. Group 2: Authentic-Dubber Framework - The Authentic-Dubber framework, developed by a team led by Professor Liu Rui, introduces a director role into AI dubbing, simulating the emotional transmission mechanisms found in traditional dubbing processes [4]. - This system aims to teach AI to "understand first, then express," moving beyond mere imitation of sounds to a more nuanced emotional delivery [4]. Group 3: Mechanisms of Authentic-Dubber - The framework includes a multi-modal reference material library that serves as an emotional guide for AI, integrating various emotional cues such as scene atmosphere and facial expressions [7]. - A retrieval-augmented strategy allows the AI to quickly access emotionally relevant reference clips, mimicking how actors internalize emotional cues under a director's guidance [11]. - The system employs a progressive graph-structured speech generation method to ensure that the final output is rich in emotional layers, enhancing the overall quality of the dubbing [13]. Group 4: Experimental Validation - In tests on the V2C-Animation dataset, Authentic-Dubber significantly outperformed all mainstream baseline models in emotional accuracy (EMO-ACC) [14]. - Subjective evaluations by human listeners showed that Authentic-Dubber achieved the highest scores in emotional matching (MOS-DE) and emotional authenticity (MOS-SE) [15]. - The system demonstrated quantifiable advantages in emotional expression, as evidenced by spectral analysis showing distinct acoustic features for different emotions [16]. Group 5: Significance of the Research - The research elevates the competitive dimension of AI dubbing from mere synchronization to emotional resonance, indicating a deeper understanding of complex emotions by AI [19]. - By simulating key interactions in human collaboration, the framework represents a significant step towards creating AI voiceovers that can truly "inject soul" into characters [19].