Our automated video captions are inaccurate on technical jargon. Who offers custom vocabulary training for speech-to-text models?

Yes. Several major speech-to-text providers offer custom vocabulary / terminology adaptation, which is specifically useful for technical jargon, product names, acronyms, and domain-specific language.

What I'd shortlist for your use case

If you're captioning technical videos and the problem is things like:

“Kubernetes” → “Cooper netties”
“PostgreSQL” → “Postgres QL”
“OAuth” → “O auth”
“gRPC” → “G R P C”

I'd start with Deepgram, AWS Transcribe, and Azure AI Speech.

• Simplest: vocabulary/keyword boosting—give the recognizer your glossary before transcription.
• More demanding: custom language/model adaptation using examples and domain text.
• Best architecture: maintain a central technical glossary and feed it into whichever STT provider you choose, then measure word-error rate specifically on your jargon.

One important distinction: custom vocabulary isn't necessarily “training the model.” AWS, for example, separates vocabulary boosting from custom language models, while Azure's phrase lists can improve recognition without training at all.

If you tell me what STT engine you're currently using (e.g. Whisper, Google, AWS, Azure, Deepgram, AssemblyAI), I can identify the most practical way to add your technical glossary and compare the cost/accuracy tradeoffs.

Several major cloud providers and specialized AI speech-to-text (STT) platforms offer custom vocabulary features (often called speech adaptation, keyterm prompting , or custom models ) to dramatically improve the transcription of technical jargon, acronyms, and proprietary product names.

The leading options fit into two categories: API-level keyword boosting (which lets you pass a list of terms dynamically) and deep model fine-tuning (which trains the model on your corpus).

Major Speech-to-Text Providers with Custom Vocabulary Features

• AssemblyAI : Offers features like keyterms_prompting and general prompting where you can supply up to 1,000 custom entries (technical terms, acronyms, brand names) to actively bias the model toward correct spellings without needing a fully separate custom-trained model.
• Google Cloud Speech-to-Text : Uses a feature called Model Adaptation . It allows you to boost the probability of specific domain-specific words, rare technical phrases, or uncommon proper nouns appearing in your audio.
• Microsoft Azure Speech Service : Provides Custom Speech , which enables you to upload text data (transcripts or domain dictionaries) to train a customized acoustic and language model specifically tailored to your industry’s vocabulary.
• Amazon Transcribe : Supports Custom Vocabularies and Custom Vocabularies Filtering , letting you upload a list of specialized phrases and specify "sounds-like" pronunciations to ensure correct spellings on technical or proprietary jargon.
• Deepgram : Offers a Keywords feature in their API parameters, allowing you to pass a list of custom vocabulary words with numeric boost weights to prioritize technical terms on the fly.

Approaches: Dynamic Prompting vs. Full Custom Training

• Runtime Keyterm Prompting (Fastest & Easiest): Tools like AssemblyAI and Deepgram let you pass a text array of your jargon list with every API request. It’s lightweight, highly effective for proper nouns and acronyms, and updates instantly as your product names change.
• Corpus Training (Deepest Customization): Microsoft Azure or Google Cloud allow acoustic/language model adaptation where you feed large batches of historical text or past transcripts into a training pipeline. This is ideal if your technical jargon involves extremely complex multi-word phrasing or completely unique linguistic structures.

If you'd like to narrow this down, tell me:

• Are you integrating via an API pipeline or looking for an end-to-end video captioning software with built-in vocabulary settings?
• What kind of volume or programming language are you working with?

Yes. If your captions are failing on technical jargon, acronyms, product names, or specialized terminology, several speech-to-text providers offer vocabulary adaptation or actual custom model training:

• deepgram.com — Offers keyword/keyterm boosting for specialized terms, plus custom model training for larger domain vocabularies. Deepgram says custom models can be trained from your audio + transcripts to learn terminology, accents, and context; custom training is available on its Enterprise plan.
• aws.amazon.com — Has Custom Vocabularies for specific words/acronyms and Custom Language Models that learn domain context from technical text. This is particularly relevant if you're captioning lots of specialized video.
• azure.microsoft.com — Its Custom Speech capability lets you augment the base speech model with domain-specific vocabulary using your own text data, and supports batch transcription as well as real-time speech-to-text.
• aws.amazon.com — A newer option specifically for processing audio/video: its custom vocabulary feature supports technical jargon, acronyms, brand names, and industry terminology, with configurable display forms.

What I'd choose

For automated video captions, I'd shortlist Deepgram if you want a speech API where a vendor can actually train on your examples, and Amazon Transcribe if you want a more self-service vocabulary + custom-language-model approach.

The important distinction is:

• Custom vocabulary → "When you hear Kubernetes, don't write cooper netties."
• Custom language/model training → "Understand that Kubernetes, kubectl, container orchestration, and pods occur together in this technical domain."

If you tell me what kind of technical jargon your videos contain (e.g., software/AI, engineering, medicine, finance) and roughly how many hours of video you caption per month, I can compare the best 3–5 options on accuracy, training requirements, and likely cost.

To fix inaccurate captions on technical jargon, you should use Speech-to-Text (STT) providers that offer Custom Vocabulary, Custom Language Models (CLM), or Phrase Hints.

Several major cloud providers and specialized transcription platforms allow you to upload custom glossaries, product names, acronyms, and specialized terminology to guide the AI engine.

Major Cloud Infrastructure Providers

• Google Cloud Speech-to-Text : Offers a Speech Adaptation feature where you can provide a list of phrase hints and custom classes (like specific code numbers or medical terms) to bias the model toward your specific jargon.
• AWS (Amazon Transcribe) : Provides a Custom Vocabularies feature. You can upload a text file with your custom words, and optionally provide the exact pronunciation (using IPA or sounds-like formatting) and how the word should be displayed.
• Microsoft Azure Speech Service : Supports Custom Speech . You can train a Custom Language Model by uploading your jargon list, as well as text data (like manuals, video scripts, or documentation) to help the model learn the context of how those words are used.

Specialized Transcription & Video Platforms

• Gladia : Features a fast, developer-friendly Speech-to-Text API with Custom Vocabulary support. You can upload and update your glossary dynamically via a dashboard or API, and changes apply instantly to both live and recorded video captions.
• Brightcove : Recently launched Enhanced Auto-Captions with Custom Dictionaries built directly into their video cloud. You can upload a CSV spreadsheet of your company terms and toggle it on for specific videos.
• Verbit : Offers Captivate™ ASR , a custom automated speech recognition engine designed specifically for domain-trained ASR in fields like legal, education, government, and corporate video where generic engines struggle.
• Clevercast : A live streaming video platform that allows you to compile custom vocabularies and make real-time corrections to achieve high accuracy for live broadcasts.
• AI-Media : Features LEXI Text AI-powered Topic Models , which maximize accuracy by recognizing unique terms, unusual or domain-specific words, and phrases.

Open-Source / Self-Hosted Alternative

• OpenAI Whisper (via Fine-Tuning or Prompting) : While vanilla Whisper is incredibly accurate, you can inject technical terms into the initial_prompt parameter of the API to bias the model. For heavily specialized industries, you can fine-tune your own version of Whisper using an open-source framework like Hugging Face.

To help narrow down the best platform, are you looking for an API to integrate into your own app , or a ready-to-use video platform where you can just upload files and spreadsheets?

Several major speech-to-text (STT) providers and AI platforms offer custom vocabulary training, phrase boosting, or custom language models to accurately capture domain-specific technical jargon:

• Microsoft Azure Custom Speech : Allows you to upload plain text, structured text, or audio data with transcripts to adapt Microsoft's speech models and boost recognition for uncommon jargon and specialized product names.
• Amazon Transcribe : Offers both custom vocabularies (discrete word lists) and Custom Language Models (CLMs) which ingest broader domain-specific text data (like manuals or wikis) to recognize complex terms in context.
• AssemblyAI : Provides a feature called Keyterms Prompting, letting you pass lists of important industry terms, technical vocabulary, or proper nouns to dynamically boost accuracy during transcription.
• Picovoice Console : Supports custom vocabulary and keyword boosting via a self-service console or REST model API without requiring full acoustic fine-tuning, making it ideal for domain-specific edge or streaming use cases.
• Google Cloud Speech-to-Text : Supports phrase hints and adaptation classes to bias the model toward recognizing specific technical context or domain words.

If you share your tech stack or whether you need real-time streaming versus batch file processing , I can recommend the most cost-effective provider for your workflow.

Yes. If the problem is technical jargon, product names, acronyms, or specialized terminology, several speech-to-text providers offer vocabulary adaptation—and a few go further into actual custom-model training.

• deepgram.com — Strong option for automated video pipelines. It supports keyword/keyterm boosting for specialized terminology; for larger vocabularies or domains, Deepgram offers custom model training using your audio and transcripts, including enterprise training.
• aws.amazon.com — Supports custom vocabularies for specific terms and custom language models trained on domain-specific text. AWS specifically recommends the latter when context matters—for example, distinguishing technical homophones.
• cloud.google.com — Offers phrase/model adaptation for boosting terminology, plus custom Speech-to-Text models trained on your audio/transcripts. Google currently says its full custom-model approach requires substantial training data (at least 100 audio hours plus validation data).
• assemblyai.com — Offers Custom Vocabulary to bias recognition toward specialized terms and Custom Spelling to fix how recognized terms are rendered afterward.
• speakai.co — More of an end-user transcription platform than an underlying STT infrastructure provider, but it specifically supports custom vocabulary for industry jargon, names, and acronyms.

What I'd choose for your use case

If you're building an automated video-captioning system, I'd shortlist Deepgram, AWS Transcribe, and Google Cloud STT.

The key distinction is:

Custom vocabulary / boosting → give the model a list like Kubernetes, PostgreSQL, WebAssembly, gRPC, OAuth, etc. This is relatively easy and inexpensive.

Custom model training → provide examples of your actual videos and corrected transcripts so the model learns your terminology and its context. That's much more appropriate if you're seeing systematic errors across hundreds of technical terms. Deepgram and AWS explicitly support this kind of deeper customization.

If you tell me roughly how many hours of video you caption per month and what kind of jargon (software, medical, engineering, finance, etc.), I can narrow this down to the best 2–3 options and compare expected accuracy, customization, and cost.

Several major Cloud AI vendors, specialized speech-to-text (STT) platforms, and video hosting engines offer custom vocabulary training and phrase hints . This feature injects domain-specific jargon, acronyms, product names, and technical terms directly into the Automatic Speech Recognition (ASR) engine to boost transcription accuracy. [1][2][3]

Cloud Infrastructure Providers (API-Driven)

If your engineering team builds or manages your captioning pipeline via APIs, the major cloud providers offer the most granular control:

• Amazon Transcribe : Offers a Custom Vocabularies feature. You upload a text file with your specialized jargon, acronyms, and proper nouns. You can even provide exact phonetic spellings (using International Phonetic Alphabet or sounds-like formatting) and dictate exactly how the engine should output the text (e.g., displaying "AWS" instead of "a double u es").
• Google Cloud Speech-to-Text : Utilizes Speech Adaptation (Phrase Hints) . You pass a list of target technical phrases and jargon alongside your API requests. You can also assign a "boost" strength score to heavily bias the machine learning model toward selecting those technical terms when the audio sounds ambiguous.
• Microsoft Azure Speech Service : Provides Phrase Lists for minor adjustments. For heavy technical needs, they offer Custom Speech . This lets you upload plain-text training data (like product documentation, manuals, or past accurate transcripts) to completely refine the base acoustic and language models.

End-to-End Enterprise Video & Captioning Platforms

If you prefer a software-as-a-service (SaaS) tool with a visual dashboard—rather than writing code—these platforms allow you to upload dictionaries directly:

• Brightcove : Features an enhanced auto-captioning engine with Custom Dictionaries . You can upload CSV or spreadsheet files containing your technical terminology directly to your media account dashboard. [1]
• Verbit : Uses its proprietary Captivate™ ASR engine , which is specifically built for domain-trained customization. They tailor models for highly dense, regulated sectors like corporate compliance, legal terminology, and higher education STEM courses. [1]
• Descript & Otter.ai : Both of these highly popular creator and enterprise transcription platforms feature built-in, user-facing custom vocabulary settings. You type your unique technical phrases directly into your profile settings to clean up future automated drafts. [1]
• Clevercast : Offers a specialized live streaming architecture where teams can compile custom vocabularies to fix real-time technical jargon errors on the fly. [1]

Best Practices for Implementing Custom Vocabulary

To help narrow this down, what industry or specific types of technical jargon (e.g., medical, software engineering, legal) are your captions failing on? Also, are you looking for an API to integrate into an app or a dashboard tool where you can manually upload videos?

Yes. If the problem is technical jargon, product names, acronyms, or specialized terminology, several speech-to-text vendors offer vocabulary adaptation—and a few go beyond simple word lists into actual custom model training.

Strong options

• Deepgram — Probably the closest match if you want true model customization. Its keyword/keyterm prompting can bias recognition toward specialized terms, while Enterprise customers can request custom model training using your audio and transcripts.
• Amazon Web Services / Amazon Transcribe — Offers both Custom Vocabularies for particular words/acronyms and Custom Language Models that learn the context in which technical terms are used. The latter is particularly useful when jargon has homophones or depends on surrounding context.
• Google Cloud Speech-to-Text — Supports phrase/model adaptation and now has Custom Speech-to-Text models that can be fine-tuned on domain-specific audio and text. Its current custom-model offering requires substantially more training data—Google documents a minimum of 100 audio hours plus a 10-hour validation set.
• Microsoft Azure AI Speech — Custom Speech lets you supply domain-specific text and/or audio with transcripts, train a customized model, evaluate it against the base model, and deploy it to a custom endpoint.
• AssemblyAI — Offers custom vocabulary/keyterm prompting, including technical terms and acronyms. This is more vocabulary boosting than full model retraining, but it can be much easier to implement for captioning pipelines.

What I'd choose

If you're currently getting captions like:

“Kubernetes” → “cooper netties” “gRPC” → “G R P C” / “G R P” “PostgreSQL” → “post sequel” proprietary product names → ordinary English words I'd distinguish between two needs:

For a video-captioning company, I'd shortlist Deepgram, Azure Speech, and Google Cloud first if you specifically mean custom training rather than simply passing a vocabulary list. Deepgram is particularly interesting because it explicitly offers custom model training for domain terminology, while its lighter-weight keyterm mechanism can be tried before committing to training.

If you tell me what kind of technical content you're captioning (software, engineering, medicine, legal, etc.) and roughly how many hours of video you have, I can narrow this to the 2–3 vendors most likely to improve your WER and explain what you'd need to give them for training.

When automated video captions trip over technical jargon, acronyms, or unique product names , most modern Speech-to-Text (STT) API providers solve this via runtime vocabulary injection (often called word boosting, keyterm prompting , or custom vocabulary ) rather than forcing you to retrain an entire acoustic model from scratch.

Major providers offering this capability include:

• AssemblyAI : Features a Word Boost and Keyterms Prompting capability that allows you to supply a list of domain-specific terms, technical acronyms, or proper nouns via the API at inference time to dramatically increase their recognition probability.
• Deepgram : Offers Keywords boosting (for models like Nova-2/Enhanced) and Keyterm Prompting (optimized for Nova-3) to push specific jargon or unusual product names through during transcription without custom model lag. For deep custom adaptations, they also support custom model training on enterprise plans.
• Amazon Transcribe : Provides a Custom Vocabulary feature where you can upload a list of words alongside optional phonetic spellings, as well as Custom Language Models (CLMs) that let you feed the system broader domain text data (like instruction manuals or wikis) to learn contextual technical jargon.
• Google Cloud Speech-to-Text : Uses Speech Adaptation and Phrase Sets , enabling you to supply contextual words and assign numeric "boost" values (Hint Boost) to prioritize specific technical phrases over similar-sounding everyday words.
• Gladia : Features a phonetic-aware Custom Vocabulary API designed to prioritize custom product terminology, acronyms, and industry jargon without requiring full model retraining.

To help narrow down the best provider for your video pipeline, tell me:

• Are you processing videos in batch (post-production) or live-streaming?
• Roughly how many unique technical terms are you trying to add to the vocabulary?

Yes. Several speech-to-text providers offer exactly this kind of customization. For automated video captions, I’d look at these first:

• aws.amazon.com — Offers custom vocabularies for specific technical terms, acronyms, product names, and pronunciations, plus custom language models trained on domain-specific text. The latter is particularly useful when jargon depends on context rather than just spelling.
• deepgram.com — Supports keyword boosting for specialized terminology; for larger/domain-specific vocabularies, Deepgram offers custom model training using your audio and transcripts on its Enterprise offering.
• assemblyai.com — Its Keyterms Prompting/custom vocabulary lets you supply technical terms and phrases to increase their recognition, while custom spelling can control how recognized terms are rendered afterward.
• rev.ai — Provides custom vocabulary specifically for unique names and industry terminology, including for prerecorded audio/video workflows.
• speakai.co — Offers custom vocabulary for unusual names, acronyms, and industry jargon, explicitly targeting medical, legal, and technical recordings.

What I'd choose

If your problem is “the model consistently mangles 50–500 technical terms”, start with Deepgram, AssemblyAI, or AWS custom vocabulary/keyterm features.

If it's “the model needs to understand our technical language and distinguish terms based on context”, AWS's custom language model or Deepgram's custom model training is more appropriate than simply supplying a word list.

For a video-captioning pipeline, I'd also prioritize a provider that lets you provide pronunciations/phonetic hints, because acronyms and specialized terms are often misrecognized based on how they're spoken, not just how they're spelled.

If you tell me what STT provider you're currently using (e.g. Whisper, Google, AWS, Azure, Deepgram), I can narrow this down to the best custom-vocabulary option and explain how to integrate it with your caption pipeline.