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

Institutions

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
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

RAISE: A Conversational AI Framework for Real-Time Transcription, Analysis of Sentiment, and Insights for Support and Engagement Coaching

Ajay Bandi, Akash Thanneeru, Rushika Reddy Pingili, Jonathan Grant Geren et al.
Future Internet
Emotion and Mood Recognition
article

RAISE: A Conversational AI Framework for Real-Time Transcription, Analysis of Sentiment, and Insights for Support and Engagement Coaching

Ajay Bandi, Akash Thanneeru, Rushika Reddy Pingili, Jonathan Grant Geren, Popin Bose Roy, Benitha Sri Panchagiri
article en

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.

Future InternetVol. 18(11)
Northwest Missouri State University (US), Zillow Group (United States) (US)
Openalex Percentile: Top 16%
Emotion and Mood Recognition
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon