RAISE: A Conversational AI Framework for Real-Time Transcription, Analysis of Sentiment, and Insights for Support and Engagement Coaching
Year: 2026
Type: article
Abstract: 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.
Source: Future Internet
DOI: 10.3390/fi18110607
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7225314270