How to Improve Video Transcription Accuracy with Custom Vocabulary

AI transcription has become incredibly powerful in 2026, with leading platforms advertising 95-98% accuracy rates. However, real-world performance varies dramatically when your videos contain technical jargon, product names, industry-specific acronyms, or unusual proper names. A medical lecture might be transcribed at 70% accuracy if the AI doesn't recognize pharmaceutical terms, while a marketing video about your SaaS product might turn "Kubernetes" into "communities" or your brand name into gibberish.

The solution? Custom vocabulary. This comprehensive guide shows you how to dramatically improve transcription accuracy by teaching AI systems the specialized words that matter to your content.

The Custom Vocabulary Advantage: Real Numbers

According to published case studies from AssemblyAI and Deepgram, adding a custom dictionary can reduce domain-specific errors by 40-60%. Here's what that looks like in practice:

Without Custom Vocabulary (Medical Lecture):

• "The patient was prescribed metformin for type two diabetes" → "The patient was prescribed met for men for type to die of bees"
• Accuracy: ~72%

With Custom Vocabulary (Medical Lecture):

• Correctly transcribes: metformin, diabetes, hemoglobin A1C, glucometer, hyperglycemia
• Accuracy: ~96%

Without Custom Vocabulary (Tech Podcast):

• "We deployed the microservices architecture on Kubernetes using Terraform" → "We deployed the micro service is arc at texture on communities using terra form"
• Accuracy: ~68%

With Custom Vocabulary (Tech Podcast):

• Correctly transcribes: Kubernetes, microservices, Terraform, Docker, PostgreSQL
• Accuracy: ~97%

The impact is substantial, especially for specialized content.

What is Custom Vocabulary in Transcription?

Custom vocabulary (also called custom dictionaries, custom word lists, or phrase boosting) allows you to provide a list of words, names, or phrases that are likely to appear in your video. When the AI transcription model encounters similar sounds, it prioritizes matching them to your custom vocabulary instead of common words.

What You Can Add:

• Company and product names: "VidNotes," "Salesforce," "ChatGPT"
• Industry jargon: "hemoglobin A1C," "OAuth," "amortization"
• Technical terms: "Kubernetes," "CRISPR," "photosynthesis"
• Proper names: "Saoirse," "Xiaoming," "Tchaikovsky"
• Acronyms: "HIPAA," "GDPR," "API," "ROI"
• Specialized vocabulary: Medical terminology, legal terms, scientific names

How It Works: AI transcription uses acoustic models (what it hears) combined with language models (what makes sense linguistically). Custom vocabulary boosts the probability of your specified words when the acoustic signal is ambiguous, helping the AI choose "Kubernetes" over "communities" when both are phonetically plausible.

Common Transcription Errors Custom Vocabulary Fixes

1. Brand and Product Names

AI models are trained on general language datasets, not your specific industry vocabulary.

Common Errors:

• VidNotes → "vid notes," "bid notes," "video notes"
• Salesforce → "sales force," "sale force"
• ChatGPT → "chat GPT," "Chat G P T," "chat jipity"
• Figma → "fig ma," "sigma"

Solution: Add exact brand spellings to your custom vocabulary, including capitalization preferences.

2. Technical Terminology

Technical fields are full of specialized words that sound like common words.

Software Engineering:

• Kubernetes → "communities," "cooper Nettie's"
• PostgreSQL → "post gray sequel," "poster SQL"
• OAuth → "oh off," "o auth"
• Git → "get," "kit"

Medical:

• Metformin → "met for men," "met four min"
• Lisinopril → "lie sin of prill," "lease in April"
• Hemoglobin A1C → "hemoglobin A one C," "hemoglobin a once he"

Legal:

• Voir dire → "vore dear," "war dear"
• Habeas corpus → "hay bee is corpse," "have bass corpus"
• Pro se → "pro say," "prose"

Solution: Create glossaries of common industry terms for your field.

3. Acronyms and Initialisms

Acronyms can be transcribed as words, spelled out, or misheard entirely.

Examples:

• API → "a P I," "epi," "a pie"
• HIPAA → "hippo," "hip a," "H I P A A"
• SQL → "sequel," "S Q L," "squeal"
• ROI → "R O I," "roy," "are oh eye"

Solution: Include both the acronym and its expanded form in your vocabulary.

4. Proper Names

People's names, especially non-English names, are frequently mangled.

Examples:

• Saoirse → "sir sha," "seer see," "search"
• Xiaoming → "show ming," "z ming"
• Niamh → "knee of," "name"
• Tchaikovsky → "chai coffee ski," "chai cough ski"

Solution: Add speaker names, interview subjects, and frequently mentioned people to your vocabulary.

5. Industry-Specific Jargon

Every industry has its own vocabulary that general AI models struggle with.

Finance:

• Amortization → "a mort is asian," "immortalization"
• Annualized → "and you lied," "annual ized"

Marketing:

• Retargeting → "re-targeting," "retreating"
• CTR (click-through rate) → "C T R," "seater"

Science:

• CRISPR → "crisper," "crisp per"
• Photosynthesis → "photo sin thesis," "photosynthetic"

Solution: Build glossaries for your specific industry and update them regularly.

How to Create an Effective Custom Vocabulary List

Step 1: Identify Problem Words

Start by transcribing a sample video without custom vocabulary and identify errors.

Process:

1. Transcribe a typical 5-10 minute video from your content
2. Review the transcript carefully
3. Highlight every error (especially repeated errors)
4. Categorize errors: brand names, technical terms, proper names, acronyms
5. Create a spreadsheet to track these words

Example Tracking Sheet:

Step 2: Build Your Core Vocabulary

Start with 20-50 of the most common specialized words in your content.

Categories to Include:

1. Your brand/company name (highest priority)
2. Your product names
3. Speaker names (especially if they have unusual names)
4. Top 10 industry terms you use most frequently
5. Common acronyms in your field
6. Competitor names (if you mention them)

Example Core Vocabulary for a SaaS Podcast:

VidNotes
Kubernetes
microservices
API
OAuth
PostgreSQL
Docker
Terraform
CI/CD
DevOps
AWS
GitHub
TypeScript
React
GraphQL
REST API
webhook
JSON
YAML
MongoDB

Step 3: Add Contextual Phrases

Some platforms support multi-word phrases, which are even more powerful than individual words.

Examples:

• "machine learning model"
• "natural language processing"
• "return on investment"
• "click-through rate"
• "user experience design"

Why This Matters: The phrase "machine learning model" is less likely to be misheard than just "machine" or "learning" individually.

Step 4: Include Variations

Add common variations, abbreviations, and related forms.

Examples:

• API, APIs, API's (possessive, though grammatically incorrect, might be spoken)
• Kubernetes, K8s (common abbreviation)
• OAuth, OAuth 2.0, OAuth2
• transcribe, transcription, transcribing, transcribed

Step 5: Test and Iterate

Custom vocabulary isn't set-it-and-forget-it. Test your list and refine it.

Testing Process:

1. Transcribe the same sample video with your custom vocabulary
2. Compare accuracy to the version without custom vocabulary
3. Identify any new errors introduced by your vocabulary
4. Add missed specialized terms
5. Remove words that caused confusion
6. Repeat until accuracy plateaus

Pro Tip: Keep track of accuracy improvements. If you go from 78% to 94% accuracy, that's quantifiable ROI for your time spent on vocabulary building.

Platform-Specific Custom Vocabulary Features

Not all transcription tools support custom vocabulary, and implementation varies.

VidNotes (Coming Soon)

VidNotes is rolling out custom vocabulary features in 2026 to help users transcribe specialized content more accurately. Expected features include:

• Upload custom word lists before transcription
• Save vocabulary templates for different content types
• Automatic learning from manual corrections
• Industry-specific pre-built vocabularies (medical, legal, tech, finance)

Current Workaround: Manually review and edit transcripts, correcting specialized terms. VidNotes saves your edits and makes future searches more accurate.

AssemblyAI

AssemblyAI offers robust custom vocabulary support via API:

{
  "audio_url": "https://example.com/audio.mp3",
  "word_boost": ["Kubernetes", "VidNotes", "OAuth"],
  "boost_param": "high"
}

Boost Levels: Low (conservative), default, high (aggressive)

Pros: Very effective for technical content; supports up to 1,000 custom words

Cons: API-only; no GUI for casual users

Deepgram

Deepgram's "keywords" parameter allows custom vocabulary:

{
  "keywords": ["Kubernetes:3", "VidNotes:5", "OAuth:2"]
}

The number after the colon indicates boost intensity (higher = more aggressive).

Pros: Granular control over boost levels per word

Cons: Requires understanding of boost parameters; API-only

Rev AI

Rev's human transcription service automatically handles specialized vocabulary better because human transcribers can research terms, but their AI service supports custom vocabulary through the API.

Pros: Hybrid human + AI option for critical accuracy

Cons: Human transcription is expensive ($1.50/min)

Otter.ai

Otter has a "Custom Vocabulary" feature in settings:

1. Go to Settings → Custom Vocabulary
2. Add words and phrases
3. Save the list
4. Future transcriptions will prioritize these words

Pros: User-friendly GUI; no coding required

Cons: Limited to ~100 words in the free tier

Sonix

Sonix offers "Custom Dictionary" in the editor:

1. Upload your file and start transcription
2. Go to Settings → Custom Dictionary
3. Add words before or after transcription
4. Reprocess the transcript with the updated dictionary

Pros: Can add vocabulary after transcription and reprocess

Cons: Reprocessing costs additional credits

Custom Vocabulary Best Practices

1. Prioritize Frequency

Focus on words that appear repeatedly in your content. A word that appears 50 times has 50x the impact of a word that appears once.

Use Frequency Analysis: Count how often specialized terms appear in your typical content.

2. Avoid Over-Boosting

Adding too many obscure words can actually reduce accuracy by causing the AI to "see" those words everywhere.

Rule of Thumb: Start with 20-50 high-frequency words, then expand to 100-200 as needed. Avoid lists of 1,000+ words unless you're transcribing highly specialized content.

3. Use Proper Capitalization

If your platform supports it, use proper capitalization in your vocabulary list:

• "VidNotes" (not "vidnotes")
• "OAuth" (not "oauth")
• "PostgreSQL" (not "postgresql")

Some platforms will preserve your capitalization in the final transcript.

4. Include Phonetic Variations

If a word has multiple common misspellings or phonetic variations, include them:

• "Kubernetes" and "K8s"
• "OAuth" and "OAuth 2.0" and "OAuth2"

5. Test with Representative Content

Always test your custom vocabulary on a representative sample of your actual content, not a best-case scenario.

6. Update Regularly

As your industry evolves, so should your vocabulary:

• Add new product names when you launch features
• Add new team members' names
• Update with emerging industry jargon
• Remove obsolete terms

Beyond Custom Vocabulary: Other Accuracy Improvements

Custom vocabulary is powerful, but it's not the only way to improve accuracy.

1. Audio Quality Matters Most

Even the best custom vocabulary can't fix poor audio. Prioritize:

• Use quality microphones (not laptop built-in mics)
• Record in quiet environments (reduce background noise)
• Use pop filters to reduce plosives (p, b, t, d)
• Record in lossless or high-bitrate formats (WAV, FLAC, or 320kbps MP3)

2. Speaker Diarization

Teaching the AI who is speaking improves accuracy:

• Assign names to speakers after transcription
• Some platforms learn from these assignments over time

3. Manual Corrections and Feedback

Some platforms learn from your manual corrections:

• Review transcripts carefully
• Fix errors manually
• The AI may learn from your corrections over time (platform-dependent)

4. Language and Accent Settings

Select the correct language variant:

• "English (US)" vs. "English (UK)" vs. "English (Australian)"
• "Spanish (Spain)" vs. "Spanish (Latin America)"

Accent-aware models perform better when the correct variant is selected.

5. Pre-Processing Audio

Use audio editing tools to enhance recordings before transcription:

• Noise reduction filters (remove hum, hiss, background noise)
• Normalization (balance volume levels)
• Compression (even out loud and quiet sections)
• High-pass filters (remove low-frequency rumble)

Tools like Audacity (free) or Adobe Audition (paid) can significantly improve audio quality.

Industry-Specific Custom Vocabulary Examples

Medical and Healthcare

Common Terms:

metformin, lisinopril, atorvastatin, omeprazole
hemoglobin A1C, blood pressure, glucose, cholesterol
diagnosis, prognosis, treatment, medication
HIPAA, EHR, EMR, ICD-10
CT scan, MRI, X-ray, ultrasound
diabetes, hypertension, hyperlipidemia
myocardial infarction, cerebrovascular accident

Software Engineering and Tech

Common Terms:

Kubernetes, Docker, microservices
API, REST, GraphQL, JSON, YAML
PostgreSQL, MongoDB, Redis
React, TypeScript, Node.js
CI/CD, DevOps, Terraform
OAuth, JWT, authentication
GitHub, GitLab, Bitbucket
AWS, Azure, Google Cloud

Legal

Common Terms:

plaintiff, defendant, deposition
voir dire, habeas corpus, pro se
tort, liability, negligence
HIPAA, GDPR, compliance
discovery, interrogatory, subpoena
affidavit, testimony, cross-examination
appellate, litigation, arbitration

Finance and Accounting

Common Terms:

amortization, depreciation, EBITDA
annualized return, ROI, IRR
assets, liabilities, equity
GAAP, IFRS, SEC, FINRA
portfolio, diversification, hedge
fiscal year, quarter, earnings call
balance sheet, income statement, cash flow

Marketing and Sales

Common Terms:

CTR, CPC, CPM, ROI, ROAS
retargeting, remarketing, conversion
SEO, SEM, PPC, organic traffic
CRM, lead generation, funnel
A/B testing, multivariate testing
engagement rate, bounce rate
attribution, customer lifetime value

Comparison: Platforms Supporting Custom Vocabulary

Frequently Asked Questions

Does custom vocabulary slow down transcription?

No. Adding custom vocabulary might increase processing time by a few seconds, but the impact is negligible. The accuracy improvement far outweighs any minor delay.

Can I use custom vocabulary for multiple languages?

Yes, if your transcription platform supports multilingual transcription. You'll need separate vocabulary lists for each language.

How many words should I add to my custom vocabulary?

Start with 20-50 high-frequency specialized terms. You can expand to 100-200 for highly technical content. Avoid going beyond 500 words unless absolutely necessary, as over-boosting can reduce accuracy.

Will custom vocabulary help with accents?

Indirectly, yes. If accent-related errors cause technical terms to be misheard, custom vocabulary helps the AI choose the correct word even when pronunciation is unclear. However, accent-specific models are better for general accent handling.

Can I share custom vocabulary lists with my team?

Most platforms allow exporting and importing vocabulary lists (usually as CSV or JSON files), making it easy to share with colleagues.

Does VidNotes support custom vocabulary?

Custom vocabulary features are coming to VidNotes in 2026. Currently, you can manually edit transcripts to correct specialized terms, and VidNotes saves your edits for better search accuracy.

Is custom vocabulary the same as speaker names?

No. Speaker names help with diarization (identifying who is speaking), while custom vocabulary helps with word accuracy. Both features improve transcription quality in different ways.

Can I add custom vocabulary after transcription?

Some platforms (like Sonix) allow you to add vocabulary and reprocess the transcript. Others (like AssemblyAI) only apply vocabulary during initial transcription.

Conclusion: Custom Vocabulary is Essential for Specialized Content

If you're transcribing general conversation, small talk, or everyday content, AI transcription works great out of the box. But if your videos contain technical jargon, product names, industry acronyms, or specialized terminology, custom vocabulary is essential.

The numbers don't lie: custom vocabulary reduces domain-specific errors by 40-60%, taking accuracy from ~75-85% to 95-98%+ for specialized content.

Action Steps:

1. Transcribe a sample video without custom vocabulary
2. Identify repeated errors (especially technical terms, brand names, acronyms)
3. Create a core vocabulary list (20-50 words to start)
4. Use a platform that supports custom vocabulary (VidNotes coming 2026, Otter.ai, Sonix, AssemblyAI, Deepgram)
5. Test and iterate, tracking accuracy improvements
6. Expand your vocabulary list as needed (aim for 100-200 words for technical content)

Whether you're transcribing medical lectures, tech podcasts, legal depositions, or marketing videos, custom vocabulary unlocks professional-grade accuracy that generic AI transcription can't match.

Ready to improve your transcription accuracy? Try VidNotes free on iOS, web (app.vidnotes.app), or install the Chrome extension. Android coming soon. Premium plans start at $9.99/month or $49.99/year, with custom vocabulary features rolling out in 2026.

Sources:

• AI Transcription Accuracy Guide 2026 - SummarizeMeeting
• Audio to Text Optimization: 2026 Guide - Medium
• AI Transcription Accuracy Benchmarks 2026 - PlainScribe
• AI Transcription Getting Words Wrong? 2026 Solutions - BrassTranscripts
• Tips on Improving Transcript Accuracy - Otter.ai
• AI Transcription Software Features - Sonix