meeting-insights-analyzer

To analyze meeting transcripts for filler words and interruptions, import Zoom, Google Meet, Otter.ai exports, or local .txt, .vtt, .srt, and .docx files with speaker labels and timestamps. The tool extracts frequency counts, timestamped examples, and actionable phrasing recommendations.

Speaking ratio analysis measures the distribution of talk time among participants in meeting transcripts. By processing speaker labels and timestamps from files like .vtt or .srt, it identifies conversation domination, facilitation quality, and active listening indicators across recorded meetings.

Yes, you can analyze Zoom and Google Meet transcripts for conflict avoidance patterns. The tool processes exported text files to detect hedging language and recurring behaviors, providing timestamped examples and concrete alternative phrasing to improve communication.

The best way to track communication trends across multiple meeting transcripts is to batch process files from a directory like ~/meetings/. The tool compares filler-word frequency, tone sentiment, and speaking ratios over time, generating trend reports for coaching and performance reviews.

Speaker labels and timestamps are required when available to accurately detect interruptions, speaking ratios, and provide timestamped examples. The tool uses these to map behavioral patterns to specific participants and generate comparative trends across meetings.

To get actionable feedback from Otter.ai meeting exports, import the transcript files for analysis. The tool identifies conflict avoidance, filler words, and facilitation issues, then outputs concrete phrasing recommendations and timestamped examples explaining why they matter.