[Ian Investor]

Hello — I’m exploring simple ways to make videos and images more accessible for people who are deaf, hard of hearing, or visually impaired. I’ve heard AI tools can generate captions, transcripts, and alt text automatically.

My main questions:

• How reliable are AI-generated captions/transcripts/alt text for accessibility?
• Which tools are easiest for a non-technical person to use?
• What steps should I take to check and edit AI output so it meets accessibility needs?
• Any privacy or common-sense tips when using these services?

I’d appreciate short, practical replies: tool names, a quick note on accuracy, and one tip for verifying or improving results. Thanks — I’m trying to build a simple, dependable workflow that doesn’t require technical skills.

[aaron]

Quick answer: Yes — AI can create accurate captions, transcripts, and meaningful alt text quickly. Done well, it reduces compliance risk, boosts engagement, and saves hours of manual work.

The problem: Manual captioning and alt-text creation is slow, inconsistent, and expensive. Many teams skip it, exposing content to accessibility complaints and losing reach.

Why it matters: Accessibility isn’t just compliance — it’s audience growth. Captions improve SEO, transcripts increase content reuse, and clear alt text helps screen-reader users and search engines. That translates into measurable traffic and engagement gains.

What I’ve learned: Off-the-shelf AI is fast but needs guardrails. The best results come from a hybrid workflow: AI for the heavy lifting, humans for quality control and context.

1. What you’ll need
   • Source audio/video files (MP4/MP3)
   • One AI tool that does speech-to-text and one that can generate alt text (many tools combine both)
   • Simple text editor or captioning tool to review and export SRT/VTT
   • Someone to do a 10–20 minute QA pass per asset
2. How to do it — step-by-step
   1. Upload file to AI speech-to-text. Export transcript + timecoded captions (SRT/VTT).
   2. Run transcript through an AI prompt to create concise captions and speaker labels.
   3. For images/frames needing alt text, give the AI a short context and ask for 1–2 sentence descriptions focused on function, not decoration.
   4. QA pass: check 3 things — speaker attribution, timestamps alignment, and sensitive/hallucinated content.
   5. Publish captions and alt text. Keep the original transcript for repurposing blog posts, social clips, and SEO.

Copy-paste AI prompt (use this exactly):

“Given the following transcript, produce concise captions formatted for .srt with accurate speaker labels and timestamps trimmed to natural pauses. Keep each caption to 1–2 lines and 35 characters per line where possible. Flag any unclear audio. Transcript: [paste transcript here].”

Metrics to track

• Time per asset (goal: cut manual time by 70%)
• Caption accuracy rate (word error rate target <10%)
• Accessibility compliance checks passed (WCAG checkpoints)
• User engagement lift (watch time, bounce rate)
• Repurposing output (number of posts/derivatives created from transcript)

Common mistakes & fixes

• Hallucinated facts in alt text — fix by adding context to the prompt (who/what/why).
• Poor speaker separation — add short speaker markers in the transcript or use manual labels during QA.
• Overly verbose captions — constrain length in the prompt (35 chars/line).

1-week action plan

1. Day 1: Pick one high-value video (customer testimonial or explainer).
2. Day 2: Run it through speech-to-text and create draft SRT.
3. Day 3: Use the prompt above to tighten captions and create alt text for any visuals.
4. Day 4: Conduct 15–20 minute QA and fix issues.
5. Day 5–7: Publish with captions/alt text, measure time saved and engagement change, iterate.

Your move.

[Jeff Bullas]

Nice point — I like your emphasis on a hybrid workflow: AI for speed, humans for context and QA. That’s the sweet spot.

Here’s a practical, step-by-step playbook you can use today to turn videos and images into accessible assets without slowing your team down.

What you’ll need

• Source files (MP4/MP3 for video/audio; PNG/JPEG for images)
• An AI tool that does speech-to-text and one (or same tool) for image descriptions
• A simple caption editor that exports SRT or VTT
• A 10–20 minute QA slot per asset (someone who knows the topic)

Step-by-step (do this in order)

1. Upload audio/video to the speech-to-text AI. Export a full transcript and a draft time-coded SRT/VTT.
2. Run the transcript through an AI prompt to tidy captions: shorten lines, add speaker labels, flag unclear audio.
3. Extract key frames or images that need alt text. Give the AI a one-line context and ask for 1–2 sentence functional descriptions (what’s important, not decorative).
4. Do a focused QA: check speaker attribution, timestamps on the first and last 30 seconds, and any possible hallucinations in alt text.
5. Publish captions and alt text. Save the transcript for repurposing (blog posts, social copy).

Copy-paste AI prompt (use this exactly)

“Given the following transcript, produce concise captions formatted for .srt with accurate speaker labels and timestamps trimmed to natural pauses. Keep each caption to 1–2 lines and about 35 characters per line where possible. Flag any unclear audio or overlapping speech. Transcript: [paste transcript here].”

Alt-text prompt (copy-paste)

“Describe this image in 1–2 sentences for a screen reader. Focus on the image’s purpose for the content (what a blind user needs to know). Mention people, actions, and important text. Do not guess identities. Image context: [paste context here].”

Example SRT snippet

1
00:00:00,000 –> 00:00:03,000
Speaker 1: Welcome to our product demo.

2
00:00:03,100 –> 00:00:06,000
Speaker 2: Today we’ll show key features.

Common mistakes & fixes

• Hallucinated details in alt text — fix: include context and restrict guesses.
• Poor speaker separation — add brief speaker markers in the transcript before running the prompt.
• Too-long captions — enforce char/line limits in the prompt and trim during QA.

3-day quick test plan

1. Day 1: Pick one high-value video and run speech-to-text.
2. Day 2: Generate captions + alt text using the prompts above.
3. Day 3: Do a 15-minute QA, publish, and measure time saved and any engagement lift.

Start small. Ship one accessible asset this week and you’ll see how quickly the process scales.

— Jeff

[Fiona Freelance Financier]

Nice summary — you’ve already got the right hybrid approach. Keep the routine simple so accessibility stops being a project and becomes part of production. Below is a tidy, low-stress workflow you can adopt immediately, with clear expectations and a short QA checklist.

What you’ll need

• Source files: MP4/MP3 for audio/video; JPEG/PNG for images
• An AI service that does speech-to-text and one that handles image descriptions (often the same platform)
• A caption editor that exports SRT or VTT
• A reviewer who understands the topic for a 10–20 minute QA pass per asset

How to do it — step-by-step

1. Upload the audio/video to your speech-to-text AI and export a full transcript plus a draft timecoded SRT/VTT.
2. Ask the AI to tidy captions (shorten lines, add speaker labels, and flag unclear or overlapping audio). Limit caption length so reading stays comfortable.
3. For images or video frames that matter, give the AI a one-line context (who/what/why) and request 1–2 sentence functional alt text focused on the purpose, not decoration.
4. Do a focused QA for 10–20 minutes: check speaker labels, spot-check timestamps (start, middle, end), and verify any descriptive detail the AI supplied.
5. Export and publish captions/alt text. Keep the original transcript for repurposing (blogs, social posts, pull-quotes).

QA checklist (quick scan)

• Speaker attribution correct and consistent
• Timestamps align at natural pauses for the opening and closing 30 seconds
• No invented facts in alt text — if the AI guessed, rewrite with context
• Reading length reasonable (1–2 lines per caption)

What to expect

1. Time savings: first-cycle automation typically cuts manual caption time by ~50–70%; QA stabilizes after a few assets.
2. Accuracy: speech-to-text will be very good for clear audio; expect more work with accents, jargon, or crosstalk.
3. Risk control: hybrid QA prevents hallucinated alt text and misattributed speakers — that’s the low-effort compliance win.

Simple 3-day test

1. Day 1: Pick one clear, high-value video and run speech-to-text.
2. Day 2: Generate captions and alt text using the guidance above; don’t over-engineer prompts — be direct about labels, length, and context.
3. Day 3: Do a 15-minute QA, publish, and measure time saved plus any engagement change. Repeat and scale.

Keep the routine small and repeatable: one clear process, one short QA window, and you’ll reduce stress while bringing your content within reach for more people.

[Jeff Bullas]

Yes — and here’s the pro-level upgrade so you get reliable captions, transcripts, and alt text in under an hour per asset. Same hybrid approach, with a few power moves that lift accuracy and cut QA time.

Why this works

• AI handles the heavy lifting fast; a short, focused QA pass removes risk.
• A tiny amount of prep (glossary + context) dramatically improves accuracy on names, jargon, and brand terms.
• Clear rules on length, pace, and purpose keep you aligned with accessibility standards.

What you’ll add to your toolkit

• A simple glossary (company names, product terms, speaker names, acronyms)
• A caption style note: target 1–2 lines, ~32–42 characters/line, insert non-speech cues like [music], [laughter]
• A one-line context template for alt text (who/what/why/how it supports the content)

Step-by-step (with pro guardrails)

1. Prep (5 minutes)
   • Create/paste a 10–20 word glossary (proper nouns, jargon). Keep it beside your AI tool.
   • Decide speakers: S1, S2, or actual names. Consistency beats perfection.
2. Transcribe (10–15 minutes)
   • Upload the media and generate a transcript + draft SRT/VTT.
   • If your tool allows, supply the glossary for better recognition.
3. Shape captions (10 minutes)
   • Trim to natural pauses. Aim for 1–2 lines, ~2–3 seconds on screen.
   • Add non-speech cues sparingly: [music], [applause], [laughter].
   • Keep reading pace comfortable: roughly 15–20 characters per second.
4. Create alt text (10 minutes)
   • For each image or key frame, write a one-line context: purpose, audience, call-to-action.
   • Ask the AI for 1–2 sentence functional descriptions. No guessing identities; describe what matters to understanding.
   • Decorative images: use empty alt (e.g., alt=””) so screen readers skip them.
5. Focused QA (10–20 minutes)
   • Spot-check timestamps at start, middle, end; fix any drift.
   • Verify speaker labels and any domain terms against your glossary.
   • Scrub alt text for invented details; ensure it answers “what does a blind user need to know here?”
6. Export + publish
   • Export SRT/VTT and store the transcript for repurposing (blog, social snippets, quotes).

Copy-paste prompt: caption shaping (SRT)

“You are an accessibility captioner. Rewrite these captions for .srt with natural pauses, accurate speaker labels, and non-speech cues. Keep each caption to 1–2 lines and ~32–42 characters per line. Target 2–3 seconds per caption and ~15–20 characters/second. Preserve meaning; remove fillers (um, uh) unless essential. Use [music], [laughter], [applause] when present. Use this glossary exactly: [paste glossary]. Return only valid .srt. Input transcript/captions: [paste here].”

Copy-paste prompt: alt text (functional)

“Write alt text in 1–2 sentences for a screen reader. Describe purpose and essential details; do not guess identities or colors if uncertain. If the image is decorative, respond with: alt="". Context (audience + why the image matters): [paste here]. Visible text in image (if any): [paste here]. Output: alt text only.”

Example outputs

• Caption snippet100:00:00,000 –> 00:00:02,600Sara: Welcome to our product demo.200:00:02,700 –> 00:00:05,400We’ll cover setup, tips, and pricing.300:00:05,500 –> 00:00:07,200[laughter] That part’s quick.
• Alt text — product shot: “Hand holding a compact smart sensor mounted on a wall, showing a green status light to indicate it’s active.”
• Alt text — chart: “Line chart of monthly sign-ups rising from 500 in January to 2,400 in June, highlighting a sharp jump after March.”
• Alt text — group photo: “Three team members seated around a laptop, smiling while reviewing a marketing report during a meeting.”

Common mistakes and quick fixes

• Too much text on screen — Cap at 1–2 lines; split at punctuation or natural pause words (and, but, so).
• Speaker confusion — Insert a simple speaker map at the top of the transcript before prompting (Sara:, Host:, Guest:).
• Hallucinated alt text — Provide context and visible text; forbid guessing. If uncertain, write what’s known or use alt=”” if decorative.
• Missing non-speech cues — Add [music] or [applause] when it affects meaning or mood.
• Numbers and acronyms — Keep numbers as spoken; spell acronyms on first use in transcripts for clarity, then abbreviate.

Quality targets (set expectations)

• Caption accuracy: word error rate under 10% for clear audio.
• Reading comfort: ~15–20 characters/second; 2–3 seconds per caption.
• Alt text: 1–2 sentences that convey purpose; avoid identity guesses and aesthetic fluff.
• QA time: 10–20 minutes per asset after two practice runs.

60-minute runbook (repeat weekly)

1. Minutes 0–10: Prep glossary + context, choose one asset.
2. Minutes 10–25: Transcribe and export draft SRT/VTT.
3. Minutes 25–40: Run caption and alt-text prompts; apply glossary.
4. Minutes 40–60: QA pass (timestamps, speakers, cues, alt text). Publish.

The reminder

Accessibility compounds. One repeatable workflow gives you compliance confidence, more viewers who stick around, and content you can repurpose again and again. Start with one asset today and lock in the habit.

[Author]

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