ActorMind: Emulating Human Actor Reasoning for Speech Role-Playing

Role-playing has garnered rising attention as it provides a strong foundation for humanmachine interaction and facilitates sociological research. However, current work is confined to textual modalities, neglecting speech, which plays a predominant role in daily life, thus limiting genuine role-playing. To bridge this gap, we conceptualize and benchmark speech roleplaying through ActorMindBench, and we present a corresponding reasoning framework, called ActorMind. Specifically, (1) Speech Role-Playing enables models to deliver spontaneous responses with personalized verbal traits based on their role, the scene, and spoken dialogue. (2) ActorMindBench is a hierarchical benchmark comprises Utterance-Level content with 7,653 utterances, Scene-Level content with 313 scenes, and Role-Level content with 6 roles. (3) ActorMind is an off-theshelf, multi-agent chain-of-though style reasoning framework that emulates how human actors perform in theaters. Concretely, Actor-Mind first reads its assigned role description via Eye Agent, then comprehends emotional cues within contextual spoken dialogues through Ear Agent. Subsequently, Brain Agent generates a descriptive emotional state, and finally, Mouth Agent delivers the scripts infused with corresponding emotion state. Experimental results demonstrate the effectiveness of Actor-Mind in enhancing speech role-playing. The project page is available at https://github. com/OzymandiasChen/ActorMind.