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.

翻译：本文解决了自动口头回应评分（AVRS）使用中的一个关键安全缺口。我们提出了一种新颖的混合框架，用于检测有困扰的学生，该框架结合了文本分类器（基于回答内容进行训练）和音频分类器（利用韵律标记进行训练）。该方法通过同时考虑回答的内容和韵律，克服了传统 AVRS 系统的关键局限性，在识别可能令人担忧的回答方面实现了更优的性能。该系统可加速人工审查流程，尤其在及时干预可能至关重要的情境下，这一能力或可挽救生命。