融合文本与语音特征，自动识别学生潜在危机发言。
Detecting Alarming Student Verbal Responses using Text and Audio Classifier
- 结合内容分析与语调特征的双模检测方法
- 通过多模态信息提升危机响应识别准确率
- 适合教育安全监控与心理干预系统部署
本文针对自动化口语回应评分（AVRS）系统在学生安全评估中的关键漏洞，提出一种新型混合框架，通过文本分类器分析回应内容，音频分类器捕捉语调特征，综合判断学生发言是否具有潜在危险性。该方法突破传统仅依赖内容分析的局限，显著提升对高风险发言的识别能力，可加速人工复核流程，在需要及时干预的场景中可能挽救生命。
原文摘要 · Abstract (English)
This paper addresses a critical safety gap in the use Automated Verbal Response Scoring (AVRS). We present a novel hybrid framework for troubled student detection that combines a text classifier, trained to detect responses based on their content, and an audio classifier, trained to detect responses using prosodic markers. This approach overcomes key limitations of traditional AVRS systems by considering both content and prosody of responses, achieving enhanced performance in identifying potentially concerning responses. This system can expedite the review process by humans, which can be life-saving particularly when timely intervention may be crucial.
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