多模态情感识别系统：整合面部表情、身体动作、语音和口语
A Multimodal Emotion Recognition System: Integrating Facial Expressions, Body Movement, Speech, and Spoken Language
AI总结：
本研究提出一种整合面部表情、身体动作、语音和口语的多模态情感识别系统，为心理评估提供客观、数据驱动的工具，降低误诊风险并提升诊断准确性。
摘要：
传统心理评估严重依赖人类观察和解释，容易出现主观性、偏见、疲劳和不一致。为解决这些局限性，本研究提出了一种多模态情感识别系统，为心理学家、精神科医生和临床医生等评估者提供标准化、客观和数据驱动的工具。该系统整合面部表情识别、语音、口语和身体动作分析，以捕捉人类评估中常被忽略的细微情感线索。通过结合这些模态，系统提供更稳健、全面的情感状态评估，降低误诊和过度诊断风险。在模拟真实条件下的初步测试表明，该系统有望提供可靠的情感洞察，以提高诊断准确性。本研究凸显了自动化多模态分析作为传统心理评估实践重要补充的前景，可应用于临床和治疗环境。
英文摘要：
Traditional psychological evaluations rely heavily on human observation and interpretation, which are prone to subjectivity, bias, fatigue, and inconsistency. To address these limitations, this work presents a multimodal emotion recognition system that provides a standardised, objective, and data-driven tool to support evaluators, such as psychologists, psychiatrists, and clinicians. The system integrates recognition of facial expressions, speech, spoken language, and body movement analysis to capture subtle emotional cues that are often overlooked in human evaluations. By combining these modalities, the system provides more robust and comprehensive emotional state assessment, reducing the risk of mis- and overdiagnosis. Preliminary testing in a simulated real-world condition demonstrates the system's potential to provide reliable emotional insights to improve the diagnostic accuracy. This work highlights the promise of automated multimodal analysis as a valuable complement to traditional psychological evaluation practices, with applications in clinical and therapeutic settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
