RAISE: A Conversational AI Framework for Real-Time Transcription, Analysis of Sentiment, and Insights for Support and Engagement Coaching
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.
Authors
- Ajay Bandi (ORCID: https://orcid.org/0000-0003-2434-736X)
- Akash Thanneeru
- Rushika Reddy Pingili
- Jonathan Grant Geren
- Popin Bose Roy
- Benitha Sri Panchagiri
Institutions
- Northwest Missouri State University (US)
- Zillow Group (United States) (US)
Publication Details
- Journal
- Future Internet
- Published
- 2026-11-05
- DOI
- https://doi.org/10.3390/fi18110607
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 0.00