The Most Common AI Transcription Mistakes (And How to Catch Them)

AI transcription can save time, but it also makes predictable mistakes that can change meaning. The fastest way to improve accuracy is to know what errors happen most often and run quick checks—search patterns, number scans, glossary review, and targeted listen-backs—to catch them. This guide lists the most common AI transcription mistakes and gives practical ways to detect, correct, and prevent each.

Primary keyword: AI transcription mistakes.

Key takeaways

• Run a “high-risk” QA pass first: names, numbers, negations, and speaker labels.
• Use simple search patterns (including regex) to find repeatable issues fast.
• Keep a project glossary for domain terms, acronyms, and preferred spellings.
• Fix the recording setup to prevent errors (mic placement, single-speaker tracks, reduce crosstalk).
• Use human review when mistakes could create legal, medical, brand, or accessibility risk.

Before you edit: a quick 10-minute QA workflow

Most transcripts do not need a full line-by-line review if you start with the most error-prone areas. Do a quick pass that tells you whether the transcript is “mostly fine” or “needs human cleanup.”

• Skim for structure: Are there reasonable paragraphs, punctuation, and line breaks?
• Check speakers: Do labels match the conversation, and do they switch at the right times?
• Scan numbers: Dates, times, prices, dosages, phone numbers, and addresses.
• Search negations: “not,” “no,” “never,” “don’t,” “can’t,” and “won’t.”
• Spot jargon: Names, acronyms, product terms, and industry phrases.
• Listen to the messy parts: Overlaps, interruptions, laughter, and low-volume moments.

If you find lots of issues in the first 2–3 minutes of audio, you will usually save time by switching to a deeper edit or getting human review.

Punctuation and paragraphing errors (run-ons, bad sentence breaks, missing commas)

AI often guesses where sentences end, especially with fast speakers or no pauses. The result can be run-on sentences, odd line breaks, or punctuation that changes meaning.

What it looks like

• Long paragraphs with no periods.
• Random sentence fragments split by periods.
• Missing commas that change intent (for example, “Let’s eat, Grandma” problems).
• Question marks missing after questions, which affects readability and captions.

How to detect it fast

• Long-line scan: Sort by paragraph length or search for lines over a certain character count (many editors show line length).
• Regex for repeated fillers: Search for patterns like \b(um|uh|you know|like)\b to find places where punctuation usually breaks down.
• Period density check: If you see a full paragraph with no “.” or “?” it likely needs re-punctuating.
• Caption readiness check: If you plan to caption, look for sentences longer than two lines on screen and split them earlier.

How to correct it

• Re-punctuate while listening at 1.25x–1.5x: Add periods at clear pauses or topic shifts.
• Use paragraph rules: Start a new paragraph when the speaker changes topic, when there is a long pause, or every 2–4 sentences.
• Confirm questions: Listen for rising intonation and add “?” where needed.

Prevention tips for recording

• Ask speakers to pause briefly between points, especially for scripted reads.
• Use a consistent mic distance so words do not drop out at the ends of sentences.
• Record in a quieter room so the model can detect pauses and sentence boundaries better.

Diarization mistakes (wrong speaker labels, merged speakers, missed speaker changes)

Diarization is the system’s best guess at “who spoke when.” It often fails when voices sound similar, people interrupt each other, or the audio quality varies between speakers.

What it looks like

• Speaker 1 and Speaker 2 switch mid-sentence.
• One speaker’s lines appear under another speaker’s name.
• Two speakers get merged into one label for long stretches.
• Speaker labels drift after an interruption and never recover.

How to detect it fast

• Turn on waveform view: Many tools show speaker blocks; look for frequent tiny alternations (often a sign of confusion).
• Listen to the first 60 seconds of each speaker: Confirm the voice-to-label mapping early before you edit the whole file.
• Search for self-references: Phrases like “as I said,” “my team,” or names (“Hi, I’m…”) can help validate who is who.
• Check turn-taking: If one speaker appears to talk for 5 minutes straight in an interview, diarization may be wrong.

How to correct it

• Relabel consistently: Pick a naming convention (Interviewer/Guest, Host/Caller) and apply it across the transcript.
• Fix the first wrong switch: Diarization errors often cascade; correcting the first drift point can fix many lines after it.
• Mark uncertainty: If you cannot confirm a section, flag it (for example, “[unclear speaker]”) rather than guessing in sensitive contexts.

Prevention tips for recording

• Use separate tracks per speaker when possible (multitrack recording).
• Ask speakers not to talk over each other during critical info (numbers, names, action items).
• Keep each speaker’s mic level steady so the model does not “think” a new person started speaking.

Homophones and near-sounds (right words, wrong meaning)

AI can choose a valid word that sounds like the correct word, especially when the word is rare or the audio is noisy. These errors slip through because spellcheck will not flag them.

What it looks like

• “their/there/they’re,” “to/too/two,” “principal/principle.”
• Industry-specific near-sounds like “cache/cash,” “complement/compliment,” “foreword/forward.”
• Names turned into common words (or the reverse).

How to detect it fast

• Search your known problem list: Keep a short list of frequent confusions in your organization and scan for them.
• Context check: If a sentence is “grammatically fine” but does not make sense, assume a homophone.
• Glossary cross-check: If your product term exists, but the transcript uses a common word, that is a flag.

How to correct it

• Listen to the word in context: Check 2–3 seconds before and after to hear the full phrase.
• Standardize terms: If your team prefers “log in” vs “login,” fix it consistently with Find/Replace.

Prevention tips for recording

• Have speakers say names and key terms slowly once, early in the recording.
• For webinars or trainings, share a term list with presenters so they use consistent phrasing.

Domain terminology and acronyms (jargon, product names, medical/legal terms)

General-purpose models often struggle with niche terminology, uncommon names, and acronyms. Even when the audio is clear, the system may output a “close” word that is wrong for your field.

What it looks like

• Acronyms expanded incorrectly or turned into similar common words.
• Drug/device names misspelled or substituted.
• Company or product names altered (“AutoDesk” vs “Autodesk,” for example).
• Terms inconsistently spelled across the transcript.

How to detect it fast

• Build a mini glossary: Create a list of expected terms (people, brands, products, locations) and verify each appears correctly.
• All-caps scan: Skim for acronyms (AAA, HIPAA, UI, KPI) and confirm each one is correct for your context.
• Consistency check: Search the term and look for variants (for example, three spellings of the same name).

How to correct it

• Use Find/Replace carefully: Fix global misspellings, but confirm you will not change a different word (word boundaries help).
• Add a term note: If you must keep the transcript verbatim, add a bracketed clarification only when your style guide allows it.

Prevention tips for recording

• Ask presenters to spell names and acronyms once (“That’s N as in Nancy…”), especially for Q&A.
• Record a clean intro: names, titles, company, and topic in the first 30 seconds.

Numerals and units (dates, money, measurements, addresses)

Numbers create outsized risk because one digit can change meaning. AI may drop digits, swap “fifteen” and “fifty,” or confuse units like “mg” and “mcg.”

What it looks like

• “$1,500” transcribed as “$15,000,” or “one five” transcribed as “five.”
• Dates inverted or simplified (“03/04” becomes “March 4” without confirming format).
• Decimals and ranges lost (“0.5” becomes “5,” “10 to 15” becomes “10 15”).
• Units missing (“miles” dropped, “percent” missing).

How to detect it fast

• Digit scan: Search for any digit [0-9] and review each instance quickly.
• Regex for money and decimals: Examples: \$\s?\d (currency), \d+\.\d+ (decimals), \b\d{1,2}[:]\d{2}\b (times).
• Unit scan: Search for common units your team uses (mg, mcg, %, mph, GB) and confirm they are present near the right numbers.
• Range scan: Search for “to” and hyphens around numbers (for example, \d+\s?(to|-)\s?\d+).

How to correct it

• Always listen to numbers: Do not “logic” your way through high-stakes values.
• Standardize formatting: Pick a style (e.g., “10–15” vs “10 to 15”) and apply it consistently.
• Confirm context: If the speaker says “oh” for zero, make sure the transcript reflects the intended number.

Prevention tips for recording

• Ask speakers to repeat key numbers once, especially in calls and meetings.
• Have speakers state units every time (“0.5 milligrams,” not “point five”).
• Use good mic gain so quiet digits at the end of phrases do not drop out.

Negations (not/no) errors that flip meaning

Negations are small words with big consequences. AI may miss “not,” mishear “can” as “can’t,” or drop “don’t,” especially when speakers talk fast.

What it looks like

• “We do need approval” when the speaker said “We don’t need approval.”
• “You can ship it today” instead of “You can’t ship it today.”
• Double negatives cleaned up incorrectly (“not uncommon” changed to “uncommon”).

How to detect it fast

• Negation search pass: Search for \b(not|no|never|don\u2019t|doesn\u2019t|didn\u2019t|can\u2019t|won\u2019t|shouldn\u2019t)\b and spot-check each hit.
• Opposite-meaning scan: Search for words like “approved,” “allowed,” “required,” “included,” and confirm they match the audio when paired with “not/no.”
• Action item review: In meeting notes, listen to any sentence that assigns a task or sets a deadline; negations hide there.

How to correct it

• Listen at normal speed: Negations can blur at higher playback speeds.
• Keep the speaker’s intent: If the audio is unclear, mark it rather than guessing in high-risk contexts.

Prevention tips for recording

• Encourage speakers to avoid talking while turning their head away from the mic.
• Reduce background noise so short words do not get masked.

Missing words during crosstalk (overlaps, interruptions, backchannels)

When two people speak at once, AI often drops words or outputs a “clean” sentence that no one actually said. This shows up a lot in lively meetings, podcasts, and interviews.

What it looks like

• Sentences that jump abruptly (“I think we should—yeah—next step is”).
• Backchannels (“mm-hmm,” “right,” “yeah”) assigned to the wrong speaker or inserted mid-sentence.
• Important words missing right when someone interrupts.

How to detect it fast

• Look for em dashes and ellipses: Lots of “—” or “…” can signal overlaps or dropouts that need audio review.
• Search for [inaudible] or blanks: If your tool inserts markers, jump to those timestamps.
• Listen where speakers overlap: Skim the waveform for two voices (or busy sections) and spot-check.
• Check for “too perfect” lines: If an argument or negotiation reads oddly polite, verify the audio; overlap can get smoothed out.

How to correct it

• Decide your goal: Verbatim transcripts keep interruptions; clean read transcripts may remove backchannels but should not change meaning.
• Use time-stamped review: Go line by line only in overlap sections instead of the full file.
• Mark unintelligible overlap: When you cannot recover words, use a consistent tag your team agrees on (for example, “[crosstalk]”).

Prevention tips for recording

• Use separate mics and tracks in group settings, even simple lavaliers.
• Ask a moderator to manage turn-taking for meetings where accuracy matters.
• Record with headphones on remote calls to avoid speakerphone echo and bleed.

When to use human review (and what to hand off)

AI transcripts work well for quick reference, but some use cases need human quality assurance. Human review helps most when the cost of a mistake is high or when the audio is hard.

Use human review when

• Meaning matters: Legal, medical, HR, compliance, safety, or financial decisions depend on the words.
• Numbers matter: Earnings calls, research data, addresses, dosages, contracts, quotes, or pricing.
• Speaker identity matters: Depositions, investigations, or any file where “who said what” must be right.
• Audio is challenging: Crosstalk, accents, low volume, heavy jargon, or noisy environments.
• You will publish it: Blogs, press quotes, captions, subtitles, and training materials need polish and consistency.

What to provide to speed up human QA

• A short glossary (names, acronyms, product terms, preferred spellings).
• The desired style (clean read vs verbatim, paragraph rules, speaker labels).
• Any “must be perfect” timestamps (numbers, decisions, key quotes).
• Context for ambiguous terms (project name, client name, location).

If you still want the speed of AI, a strong approach is “AI first, human second” for targeted cleanup. For example, you can generate a draft with AI and then have it checked for the high-risk categories above.

Common questions

How accurate is AI transcription in general?

Accuracy depends heavily on audio quality, number of speakers, background noise, accents, and domain terminology. Treat AI output as a draft unless your use case can tolerate errors.

What is the fastest way to proof an AI transcript?

Start with a targeted pass: numbers, negations, names/terms, and speaker labels. Then listen only to messy sections like overlaps and low-volume audio.

Why does AI get speaker labels wrong?

Diarization relies on voice differences and clean turn-taking. Similar voices, interruptions, and varying mic quality make the system guess wrong.

How do I catch number errors without listening to the whole file?

Search for digits and review each instance, then spot-check with audio for high-stakes values. Add regex searches for currency, decimals, times, and ranges to move faster.

Should I edit punctuation for a verbatim transcript?

Yes, within your style rules. Even verbatim transcripts usually need punctuation and paragraphing so readers can follow the speaker’s meaning.

How can I reduce crosstalk errors?

Use separate mics or tracks, have a moderator manage turn-taking, and avoid speakerphone setups that increase bleed and echo. If overlap is unavoidable, plan for human review of those sections.

What should I do when the audio is unclear?

Do not guess on sensitive content. Mark the section consistently (for example, “[inaudible]” with a timestamp) and, if needed, send it for human review.

Need quality assurance beyond AI?

If you use AI transcription for speed, you can still protect accuracy with a strong QA step. GoTranscript can help with end-to-end professional transcription services or targeted cleanup when you already have a draft and just need it checked for speaker labels, numbers, terminology, and meaning.