Many modern speech-to-text (STT) and automated captioning providers offer custom vocabulary features (often called keyterm prompting, keyword boosting , or speech adaptation ) to help models accurately recognize technical jargon, acronyms, and product names without requiring full model retraining.
The leading platforms providing these capabilities include:
AssemblyAI : Offers Keyterms Prompting to boost recognition for industry-specific words or phrases, alongside a Custom Spelling feature that lets you map incorrectly transcribed or phonetic words directly to the exact proper spelling or acronym format you want in the final output.
Deepgram : Features (for Nova-3 and Flux models) to instantly increase recall rates for specialized jargon and brand terminology, as well as a legacy parameter for boosting terms on other model tiers.
We are having trouble with technical terms in our automated captions. Can you recommend speech-to-text services that allow for custom vocabulary training?
Rev AI : Provides a dedicated Custom Vocabulary API supporting both streaming and asynchronous transcription jobs, allowing you to submit thousands of domain-specific phrases per job.
Google Cloud Speech-to-Text : Uses Speech Adaptation , letting you pass phrase hints and boost values in your requests to bias the model toward specific technical vocabulary.
AWS Transcribe : Supports Custom Vocabularies , enabling you to upload a list of preferred spellings, acronyms, and technical terms so the transcription job prioritizes them.
If you want to narrow this down, let me know:
Are you looking for a developer API to build a pipeline, or an end-user captioning tool?
What programming language or environment are you integrating with?
I can help recommend the best fit for your workflow.
Yes. If your captions are failing mainly on technical jargon, product names, acronyms, and domain-specific terms, several speech-to-text providers offer vocabulary adaptation. The key distinction is that most don't literally retrain the model for you—they bias/prompt the existing model toward your vocabulary.
Provider
Customization
Good fit
Amazon Transcribe
Custom Vocabulary + Custom Language Models; supports pronunciation/display forms
Best if you have a substantial technical corpus and need deeper domain adaptation.
Deepgram
Keyterm Prompting for up to 100 important terms; enterprise customers can request custom models
Strong option for automated video/streaming transcription where jargon accuracy matters.
AssemblyAI
Custom vocabulary / keyterm prompting; up to 100 custom terms in its streaming implementation
Good developer-friendly API if you primarily need a curated jargon list.
Microsoft Azure Speech
Phrase lists for lightweight boosting; Custom Speech for actual model customization
Good if you want to move from simple vocabulary hints to training with your own text/audio.
Google Cloud Speech-to-Text
Phrase boosting/adaptation and custom model fine-tuning
Worth evaluating for large-scale captioning with substantial domain data.
What I'd choose for your use case
If you're building an automated video-captioning pipeline, I'd shortlist:
Deepgram — easiest first experiment with a list like Kubernetes, kubectl, PostgreSQL, gRPC, OAuth, WebAssembly...; its current Keyterm Prompting is specifically designed for important terminology and industry jargon.
Amazon Transcribe — stronger candidate if you have lots of domain-specific training text and want a custom language model rather than just boosting individual words.
Azure Custom Speech — particularly interesting if you have labeled audio/captions and actually want to adapt the model to your organization's terminology and speaking style.
AssemblyAI — attractive if you want a straightforward API and a relatively small set of critical terms.
One important point: "custom vocabulary" ≠ "training." If Kubernetes keeps becoming "cooper netties," vocabulary boosting may solve it. If the model consistently struggles with an entire technical domain, accents, context-dependent terminology, or specialized speech patterns, you should look for custom model/domain adaptation instead. AWS explicitly distinguishes these two approaches.
If you tell me which STT engine you're currently using (Whisper, Google, Azure, AWS, Deepgram, etc.) and roughly how many technical terms you have, I can narrow this to the best 2–3 options and compare pricing, API integration, and actual customization depth.
Yes. Several speech recognition APIs let you customize recognition for specialized industry jargon, including technical terms, acronyms, product names, medical terminology, and uncommon proper nouns.
The important distinction is that some APIs let you supply a custom vocabulary, while others let you train a model on domain-specific text or audio. If your goal is to improve transcription accuracy, both approaches can help, but they solve slightly different problems.
Best speech recognition APIs for custom vocabulary
1. Amazon Transcribe
(Strong all-around option)
Supports custom vocabularies for specific words, pronunciation hints, acronyms, and preferred written forms. It also offers custom language models that learn domain-specific context from text data, making it useful when jargon is frequently confused with ordinary words.
Best for: Enterprise transcription, healthcare terminology, finance, engineering, and large-scale audio processing.
Uses model adaptation, which lets you supply words and phrases that the recognizer should favor. Phrase sets, custom classes, and configurable boost values can help with rare terms and recurring jargon. Support varies by model and language.
Best for: Real-time transcription, multilingual applications, and terminology that can be captured in lists of words or phrases.
Offers Custom Speech, allowing you to improve a base recognition model using domain-specific text. Custom models can be used for real-time speech-to-text and batch transcription.
Best for: Organizations already using Microsoft Azure, or applications needing a model adapted to a particular technical domain.
Worth evaluating for API-first speech applications, particularly where you need low-latency transcription and terminology customization. Check the current model-specific support for keyterm prompting or vocabulary features before choosing it for a particular jargon-heavy workload.
Best for: Real-time voice applications and developer-focused transcription pipelines.
Provides custom vocabulary features that boost the likelihood of recognizing specified words and phrases, plus custom spelling to standardize how terms appear in transcripts. These are distinct capabilities: vocabulary affects recognition, while spelling corrects the rendered text.
Best for: Developers who want a straightforward transcription API with terminology controls.
The features above are documented by the respective providers.
What I would recommend
If you have a list of 50–500 industry terms: Start by testing Deepgram, Google Cloud, and Amazon Transcribe with the same audio and terminology list.
If the terms are frequently confused with ordinary words: Amazon Transcribe's custom language models or Azure Custom Speech are worth investigating because they support deeper domain adaptation.
If you have lots of historical transcripts: Prioritize solutions that can learn from domain-specific text or audio, rather than relying only on a dictionary of terms.
If you need live transcription: Compare streaming latency as well as recognition accuracy; vocabulary customization alone doesn't guarantee the best real-time experience.
How to evaluate accuracy
Use a small, representative test set containing real recordings with your specialized vocabulary. Run each API with and without customization, then compare:
Word error rate (WER): Overall transcription errors.
Keyword recall: How often important industry terms are recognized correctly.
False substitutions: Whether boosting a jargon term causes ordinary words to be transcribed incorrectly.
Latency and cost: Particularly important for real-time applications.
One caveat: adding terms to a vocabulary generally isn't the same as retraining the underlying acoustic model. If a term is consistently misheard because of pronunciation, accents, or noisy audio, a larger vocabulary alone may not solve it.
What industry are you targeting? For example, healthcare, legal, manufacturing, finance, or software engineering. I can narrow this to the APIs best suited to that jargon and whether they support a simple vocabulary upload or actual model training.
Yes, major speech-to-text (STT) providers offer robust features to handle specialized industry jargon, technical acronyms, and uncommon brand names.
Depending on your needs, solutions range from simple runtime phrase lists/hints (lightweight and fast) to full custom language model fine-tuning (deep data training for heavy domain-specific context).
Google Cloud Speech-to-Text
Mechanism: Uses via and .
Deepgram
Yes, keyterm prompting
Verify model-specific customization needs
AssemblyAI
Yes, custom vocabulary
Check current model-specific options
Speech Adaptation
Phrase Hints
Custom Classes
How it works: You supply a list of contextual words or phrases and assign them "boost values" to mathematically tell the model to favor those terms during recognition. You can also define custom classes (e.g., specific part numbers or medical device names).
Best for: Quickly nudging the engine to properly spell product names, acronyms, or localized terms without training a separate model.
Mechanism: Full Custom Speech Fine-tuning (Acoustic and Language adaptation).
How it works: Allows you to upload text data (transcripts, domain documents, manuals) to train a custom language model, and optionally audio-plus-transcript data to adapt the acoustic model for heavy background noise or distinct accents. It provides quantitative Word Error Rate (WER) testing to measure improvements.
Best for: Deep enterprise deployments in legal, medical, or technical fields requiring high adaptation depth.
Mechanism: Dual approach via Custom Vocabularies and Custom Language Models (CLM).
How it works:
Custom Vocabularies: A lightweight text/table list (up to 50 KB per file) defining specific phrases, pronunciations, and preferred display formatting (e.g., mapping a phonetic sound to an acronym).
Custom Language Models: Accepts up to 2 GB of domain-specific text data (whitepapers, manuals, logs) to teach the model how industry jargon flows naturally in sentences.
Best for: Feeding massive amounts of text documentation to an AWS-backed pipeline.
Mechanism: Real-time context injection and phonetic similarity scoring.
How it works: Newer ASR platforms allow developers to pass contextual keyword lists dynamically at runtime, optimizing recognition on the fly for front-line workers or meeting transcriptions.
Best for: Lightweight, high-speed applications where you want to update vocabulary per-request without managing persistent model training jobs.
If you'd like, let me know:
I can help you select the most cost-effective API option for your project.
