BrassTranscripts logo
BrassTranscripts
BY COPPER SUN
← Back to Blog
•11 min read•BrassTranscripts Team

10 Common Transcription Mistakes and How to Fix Them

Transcription TroubleshootingAudio Quality TipsTranscription Accuracy

Every transcript has room for improvement. Whether you're using AI transcription or reviewing human-generated transcripts, the same mistakes show up repeatedly. This guide covers the 10 most common transcription mistakes and gives you actionable fixes for each one.

Use this as a checklist before publishing, sharing, or analyzing any transcript.

Quick Navigation

Mistake #1: Poor Audio Quality

The Problem: Transcription accuracy depends heavily on audio quality. Background noise, echo, low volume, and poor microphone placement cause more errors than any other factor.

Common Symptoms:

  • Garbled or nonsensical phrases
  • Missing sections of dialogue
  • "[inaudible]" markers throughout
  • Inconsistent accuracy across the transcript

How to Fix It:

Before recording (prevention):

  • Use a dedicated microphone, not laptop built-in mics
  • Record in quiet environments away from HVAC systems
  • Position microphone 6-12 inches from speakers
  • Test audio levels before starting

After recording (damage control):

  • Use audio enhancement software to reduce noise
  • Listen to problem sections at reduced speed
  • Fill in obvious gaps manually where context is clear
  • Mark uncertain sections for verification

Related resource: Audio Quality Tips for Better Transcription

Mistake #2: Wrong Speaker Labels

The Problem: Speaker identification errors make transcripts confusing or misleading. Quotes get attributed to the wrong person, and following conversations becomes difficult.

Common Symptoms:

  • Speaker labels switch mid-conversation
  • Same speaker labeled differently throughout
  • Generic labels ("Speaker 1") instead of names
  • Overlapping speech attributed to wrong person

How to Fix It:

During transcription setup:

  • Provide speaker names and roles upfront if your service supports it
  • Use separate audio channels for each speaker when possible
  • Have speakers introduce themselves at the start of recording

After transcription:

  • Search for each speaker label and verify attribution
  • Cross-reference with video (if available) for visual confirmation
  • Use context clues: who would logically say this?
  • Fix labels systematically from start to finish

Pro tip: When speakers have similar voices, note distinguishing characteristics (accent, speaking pace, vocabulary patterns) to help with verification.

Mistake #3: Missing Words and Phrases

The Problem: Transcripts skip words, especially when speakers talk quickly, mumble, or use informal speech patterns.

Common Symptoms:

  • Sentences that don't make grammatical sense
  • Missing articles ("the," "a," "an")
  • Dropped connector words ("and," "but," "so")
  • Incomplete thoughts or trailing sentences

How to Fix It:

Identify problem areas:

  • Read transcript aloud—missing words become obvious
  • Look for unusually short sentences
  • Check transitions between speakers

Repair strategies:

  • Listen to original audio for missing sections
  • Add words in brackets [like this] when inferred from context
  • Use ellipsis (...) for genuinely inaudible content
  • Don't guess on critical content—verify or mark as uncertain

Mistake #4: Homophone Confusion

The Problem: Words that sound alike but have different meanings get swapped. AI transcription is particularly susceptible to this.

Common Examples:

  • "their/there/they're"
  • "your/you're"
  • "its/it's"
  • "affect/effect"
  • "principle/principal"
  • "complement/compliment"
  • Industry-specific: "patients/patience," "council/counsel"

How to Fix It:

Search-and-verify approach:

  1. Search for commonly confused words in your transcript
  2. Check each instance against context
  3. Fix systematically rather than on first encounter

Context-based verification:

  • Read the full sentence—does it make sense?
  • Consider the speaker's intent
  • For technical content, verify against source materials

Domain-specific awareness:

  • Legal transcripts: "statute/statue," "council/counsel"
  • Medical transcripts: "ileum/ilium," "mucus/mucous"
  • Business transcripts: "capital/capitol," "principal/principle"

Mistake #5: Punctuation Errors

The Problem: Incorrect punctuation changes meaning and makes transcripts harder to read. AI transcription often struggles with sentence boundaries.

Common Symptoms:

  • Run-on sentences spanning multiple thoughts
  • Periods in the middle of sentences
  • Missing question marks on questions
  • Comma splices creating confusing passages

How to Fix It:

Read for natural pauses:

  • Periods go where speakers take full stops
  • Commas indicate brief pauses within thoughts
  • Question marks follow rising intonation

Structural fixes:

  • Break run-on sentences at logical points
  • Match punctuation to speaker intent, not strict grammar
  • Use em dashes (—) for interrupted thoughts
  • Add paragraph breaks for topic changes

Balance accuracy with readability: Verbatim transcripts preserve every pause; clean transcripts prioritize clarity. Know which you need.

Mistake #6: Technical Terminology Mistakes

The Problem: Industry jargon, product names, acronyms, and specialized vocabulary often get mangled in transcription.

Common Symptoms:

  • Product names spelled phonetically
  • Acronyms expanded incorrectly or not at all
  • Medical/legal/technical terms garbled
  • Company-specific terminology misheard

How to Fix It:

Build a reference list:

  • Create a glossary of expected terms before review
  • Include correct spellings for names, products, companies
  • Note common misheard variations

Search-and-replace strategically:

  • Search for phonetic misspellings of key terms
  • Replace consistently throughout document
  • Verify context—same sound might be different words

Examples of common AI mishearings:

  • "Kubernetes" → "Cooper Netties"
  • "OAuth" → "O Auth" or "oh auth"
  • "MongoDB" → "Mongo DB" or "mango DB"
  • Person names: highly variable, always verify

Mistake #7: Timestamp Misalignment

The Problem: Timestamps drift out of sync with actual audio, making it difficult to locate specific moments in recordings.

Common Symptoms:

  • Timestamps don't match audio when spot-checked
  • Drift increases toward end of transcript
  • Timestamps missing entirely for some sections
  • Inconsistent format (HH:MM:SS vs MM:SS)

How to Fix It:

Verification process:

  1. Spot-check timestamps at beginning, middle, and end
  2. Note the drift pattern (consistent offset vs. increasing)
  3. Determine if systematic adjustment can fix it

Correction approaches:

  • Consistent offset: Adjust all timestamps by fixed amount
  • Increasing drift: May need manual correction or re-transcription
  • Format inconsistency: Standardize to one format throughout

When timestamps matter most:

  • Video subtitles (SRT/VTT files)
  • Legal depositions
  • Research interviews requiring precise citations
  • Content with time-based references

Mistake #8: Filler Word Overload

The Problem: Verbatim transcripts include every "um," "uh," "like," "you know," and "basically"—making text difficult to read and obscuring the actual message.

Common Symptoms:

  • Sentences cluttered with filler words
  • Speaker intent buried under verbal tics
  • Readability suffers despite accurate transcription
  • Professional documents sound unprofessional

How to Fix It:

Decide on transcription style first:

  • Verbatim: Keep all filler words (legal, research, analysis)
  • Clean verbatim: Remove fillers but preserve meaning
  • Edited: Polish for publication or professional use

Cleaning approach:

  1. Remove filler words that don't add meaning
  2. Keep fillers that indicate hesitation or uncertainty (when relevant)
  3. Preserve speech patterns for speaker characterization (when needed)

Common fillers to address:

  • "Um," "uh," "er"
  • "Like" (when not comparative)
  • "You know," "I mean"
  • "Basically," "actually," "literally"
  • "Kind of," "sort of"

Caution: Don't over-edit. Removing too many fillers can change the speaker's meaning or tone.

Mistake #9: Formatting Inconsistencies

The Problem: Inconsistent formatting makes transcripts look unprofessional and harder to navigate.

Common Symptoms:

  • Speaker labels formatted differently throughout
  • Inconsistent capitalization
  • Mixed timestamp formats
  • Paragraph breaks in random places
  • Inconsistent handling of numbers (5 vs. five)

How to Fix It:

Establish formatting rules:

  • Speaker labels: "John:" or "JOHN:" or "[John]" (pick one)
  • Numbers: Spell out one through nine, use numerals for 10+
  • Timestamps: [HH:MM:SS] or (MM:SS) (pick one)
  • Paragraph breaks: New speaker or topic change

Systematic formatting pass:

  1. Fix speaker labels first (easiest to search)
  2. Standardize timestamps
  3. Apply number formatting rules
  4. Add paragraph breaks for readability
  5. Final consistency check

Mistake #10: Not Reviewing Before Use

The Problem: Using transcripts without review leads to embarrassing errors, misattributed quotes, and unreliable documentation.

Common Symptoms:

  • Publishing content with obvious errors
  • Citing quotes that speakers didn't actually say
  • Making decisions based on misheard information
  • Damaging credibility with stakeholders

How to Fix It:

Tiered review based on use case:

Use Case Review Level
Internal notes Skim for major errors
Content repurposing Full read-through
Published quotes Verify against audio
Legal/medical Professional review recommended

Minimum review checklist:

  • Spot-check 3 random sections against audio
  • Verify all proper nouns (names, companies, products)
  • Read key quotes in full context
  • Check speaker attribution on important statements

When to invest more time:

  • High-stakes content (legal, medical, public)
  • Direct quotes that will be published
  • Content that will be cited as a source
  • Documents with potential liability

Quick-Reference Checklist

Copy this checklist for every transcript you review:

## Transcript Review Checklist

**Audio Quality**
- [ ] No significant [inaudible] sections
- [ ] Background noise didn't cause errors
- [ ] All speakers captured clearly

**Speaker Attribution**
- [ ] All speakers correctly identified
- [ ] Labels consistent throughout
- [ ] No mid-sentence speaker switches

**Content Accuracy**
- [ ] Spot-checked 3 sections against audio
- [ ] Technical terms spelled correctly
- [ ] Proper nouns verified
- [ ] Homophones checked in context

**Formatting**
- [ ] Consistent speaker label format
- [ ] Timestamps accurate (if applicable)
- [ ] Paragraph breaks at topic changes
- [ ] Numbers formatted consistently

**Final Review**
- [ ] Read-through for readability
- [ ] Key quotes verified against audio
- [ ] Appropriate for intended use

AI Prompts for Fixing Transcripts

Use these existing prompts from our AI Prompt Guide to help fix common transcript issues:

For Quality Analysis

Use the Transcript Quality Analyzer prompt to identify potential issues before manual review. This prompt scans your transcript and flags:

  • Possible speaker attribution errors
  • Sections that may need audio verification
  • Formatting inconsistencies
  • Technical terms that may be misspelled

📖 View Markdown Version | ⚙️ Download YAML Format

For Speaker Attribution Fixes

Use the Speaker Attribution Error Corrector prompt when you've identified speaker labeling issues. This prompt helps:

  • Analyze context to suggest correct attribution
  • Identify patterns in speaker confusion
  • Systematically fix labels throughout the transcript

📖 View Markdown Version | ⚙️ Download YAML Format

Prevention is Better Than Correction

The best way to fix transcription mistakes is to prevent them. Most errors trace back to audio quality issues that could have been avoided.

Before your next recording:

  1. Test your microphone and audio levels
  2. Record in a quiet environment
  3. Position microphones properly
  4. Brief speakers on clear speaking (no crosstalk)

Related resource: Audio Quality Tips for Better Transcription covers recording best practices in detail.

Frequently Asked Questions

Why does my transcript have so many errors?

Most transcription errors stem from audio quality issues, not the transcription service itself. Background noise, low microphone volume, multiple overlapping speakers, and poor recording equipment are the primary causes. Improving your recording setup typically has a bigger impact than switching transcription services.

Can AI fix transcription mistakes automatically?

AI can help identify and fix certain transcription mistakes, particularly speaker attribution errors and formatting inconsistencies. However, AI works best as an assistant rather than a replacement for human review. Use AI prompts to flag potential issues, then verify critical content manually.

How do I know if my transcript quality is good enough?

Review a sample section against the original audio. Check for missing words, incorrect speaker labels, and garbled phrases. For professional use (legal, medical, publishing), consider having a second person review critical sections. For internal notes or content repurposing, minor errors may be acceptable.

What causes speaker identification errors?

Speaker identification errors typically occur when speakers have similar voice characteristics, when speakers interrupt or talk over each other, or when audio quality makes voices difficult to distinguish. Recording in quieter environments and using separate microphones for each speaker significantly improves accuracy.

Need accurate transcripts from the start? Upload your audio to BrassTranscripts and get transcripts with automatic speaker identification. Professional-grade accuracy, 99+ languages, results in minutes.

Ready to try BrassTranscripts?

Experience the accuracy and speed of our AI transcription service.

Start Transcribing →Read More Articles
10 Common Transcription Mistakes and How to Fix Them