V-EXAM – Voice-based Explanation Assessment Model

Voice-based assessments are increasingly used in online examinations and interview evaluation systems to analyze candidate’s communication ability and subject knowledge. Traditional evaluation methods rely on human examiners, which can lead to inconsistencies, bias, and increased evaluation time [2], [3]. Recent research has shown that automatic scoring systems using speech processing, natural language analysis, and multimodal cues can significantly improve assessment efficiency and accuracy [1], [4]. In addition, techniques such as speech emotion recognition help in analyzing vocal characteristics and communication behaviour during spoken responses [5]. This project proposes V-Exam, a voice-based automated evaluation system that uses speech recognition and natural language processing to analyze spoken answers provided by candidates. The system converts voice responses into text, evaluates them for semantic relevance and clarity, and generates an objective performance score. The proposed approach aims to reduce human effort, improve scoring consistency, and enable efficient real-time voice-based assessment.