Public speaking is a vital skill for academic and professional success, yet numerous learners struggle with glos- sophobic bia, limited vocabulary, and weak delivery. Being training approaches, similar to practices or peer evaluation, give limited feedback and aren't scalable. This paper introduces Lingo-Test, an AI-powered platform designed to enhance spoken English through real-time, multimodal feedback. The system integrates Automatic Speech Recognition (ASR), Natural Lan- guage Processing (NLP), aspect discovery, and facial expression analysis to estimate both verbal and non-verbal performance. Unlike conventional tools that concentrate only on pronunciation or alphabet, Lingo-Test generates a compound confidence score and individualized recommendations covering ignorance, tone variation, vocabulary precarious, and eye contact. The architec- ture includes modular factors for speech processing, sentiment analysis, and feedback visualization, making it scalable for aca- demic and professional operations. An pilot study with learners indicated measurable advancements in ignorance, confidence, and non-verbal delivery. These results punctuate the eventuality of AI- driven multimodal feedback systems to reduce anxiety, strengthen tone mindfulness, and ameliorate communication chops, situatingLingo-Test as a practical result for education and training. \\
Thapa et al. (Thu,) studied this question.
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