filler word detection for word choice is transforming how professionals understand and improve their communication. Traditional speech coaching requires expensive one-on-one sessions and relies on memory — which means patterns go undetected for months.
Knowing how to detect word choice is one thing — doing it consistently is another. Most professionals are aware of their weaknesses in general terms but cannot pinpoint the specific moments where word choice breaks down. Without objective analysis of actual conversations, improvement remains based on guesswork and memory, which are unreliable.
SpeakFlare addresses filler word detection for word choice by automatically analyzing every Zoom and Google Meet recording. The platform transcribes each word using AssemblyAI with word-level timestamps, identifies each speaker, and runs AI analysis across four categories: grammar errors, filler words, clarity issues, and presentation effectiveness. You receive a detailed report within minutes of your meeting ending.
What makes this approach effective is the consistency. Every meeting is analyzed — not just the ones you remember to review. Over time, SpeakFlare builds a comprehensive picture of your communication patterns, showing exactly which word choice issues are improving and which persist. The progress tracking dashboard visualizes trends across weeks and months, turning abstract communication goals into measurable data.
How to measure filler words
The core metric is your filler word rate — the share of your spoken words that are fillers:
Worked example: a transcript of 1,420 words containing 34 fillers:
The second metric, fillers per minute, normalizes for how long you spoke:
What counts as high?
| Filler rate | Fillers / min | How it reads |
|---|---|---|
| Under 1% | 0–1 | Polished, confident |
| 1–2% | 1–3 | Natural, professional |
| 2–4% | 3–6 | Noticeable to listeners |
| Over 4% | 6+ | Distracting, reads as uncertain |
Typical filler words
um · uh · like · you know · basically · literally · actually · right · so · well
To detect word choice effectively, track three consecutive meetings and focus on the pattern, not individual instances. SpeakFlare's trend view shows which specific aspects of word choice are improving and which need more attention.