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RAISE (Real-time AI Insights for Sentiment and Engagement) is a preliminary framework that combines speech transcription with Whisper, speaker diarization with pyannote.audio, text-based emotion classification with DistilRoBERTa, and feedback generation with GPT-3.5. We examined the workflow using 53 audio clips: 12 author-recorded conversations, 23 YouTube clips, and 18 clips from public emotion-labeled datasets, including RAVDESS and IEMOCAP. The experiments provide an initial demonstration of how these components can be integrated and identify speaker diarization as a major source of processing latency. Because the dataset is small and the evaluation is exploratory, the results do not establish generalizable accuracy or reliable real-time performance. Further evaluation using larger, independently labeled datasets and longer conversations is needed.
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Bandi et al. (2026) studied this question.