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

Yes. What you’re looking for is usually called custom vocabulary, phrase boosting, speech adaptation, or—at the deeper end—custom language-model training.

For automated video captions, these are the strongest options I found:

Amazon is particularly relevant if by “training” you mean actually improving recognition rather than simply find-and-replace after transcription. Its custom vocabulary handles specific words/acronyms, while its custom language models can learn domain context from larger bodies of technical text.

For a video-captioning company, I'd shortlist CaptionHub, Vimeo, and Amazon Transcribe. CaptionHub explicitly supports technical jargon and product names through custom dictionaries, while Vimeo lets teams maintain custom vocabulary directly within its video workflow.

If you tell me what your current caption/STT provider is (e.g. Whisper, AWS, Google, Azure, Deepgram, etc.), I can narrow this down to vendors that can actually train/customize that model or integrate with your existing pipeline, rather than just offering a separate transcription service.

Several major speech-to-text (STT) API providers and developer platforms offer custom vocabulary training, runtime word boosting, or custom language models to fix inaccuracies with technical jargon, acronyms, and brand names:

• AssemblyAI : Provides a Keyterm Prompting / custom vocabulary feature that allows you to boost the likelihood of specific technical jargon, proper nouns, or product names being transcribed correctly without needing heavy model fine-tuning.
• Deepgram : Offers customizable speech models and industry-tuned variants (such as options optimized for medical, legal, or financial domains) alongside features to adapt to specific vocabularies.
• Google Cloud Speech-to-Text : Features Speech Adaptation , letting you pass phrase hints and boost words at runtime, as well as train dedicated custom speech models using domain-specific text corpora.
• Amazon Transcribe : Supports Custom Vocabularies (where you can define specific spellings, display forms, and phonetic "sounds like" pronunciations) as well as Custom Language Models (CLMs) trained on raw text data like manuals or wikis.
• Microsoft Azure AI Speech : Uses Phrase Lists for instantaneous runtime boosting of uncommon words, and allows full Custom Speech training by uploading text or audio datasets.
• Speechmatics : Delivers high-accuracy domain adaptation through custom dictionary capabilities across a broad array of languages.
• Rev AI : Provides a dedicated Custom Vocabulary API allowing developers to submit lists of domain-specific phrases or terms per transcription job.

If you share your current tech stack or STT vendor , or whether you need real-time streaming vs. batch video file processing , I can give you a more specific implementation guide or code snippet for your workflow.

Several top-tier speech-to-text (STT) and AI infrastructure providers offer custom vocabulary training, phrase biasing, or domain adaptation features to help models correctly identify specialized technical jargon, acronyms, and product names.

The primary platforms providing these capabilities include:

• AssemblyAI : Offers Keyterms Prompting and custom vocabulary features via their AssemblyAI Speech-to-Text API . You can pass domain-specific technical terms, brand names, or jargon directly into the API request to heavily bias the model toward correct spellings.
• Amazon Transcribe : Supports Custom Vocabularies (discrete lists of specialized words and preferred display forms) as well as Custom Language Models (CLMs) . CLMs allow you to upload large, domain-specific text corpora (such as technical manuals, wikis, or existing transcripts) so the model learns technical context without needing audio files.
• Google Cloud Speech-to-Text : Utilizes Speech Adaptation and Custom Speech Models . You can supply phrase "hints" and boost weights for particular technical vocabularies or train a fully customized model using domain-specific text and audio sets via the Google Cloud Speech-to-Text Console.
• Microsoft Azure Speech Services : Features Custom Speech , which enables you to fine-tune base speech recognition models using proprietary text data (utterances and domain dictionaries) and acoustic recordings to drastically lower Word Error Rates (WER) on dense jargon.
• Rev AI : Provides an explicit Custom Vocabulary API allowing developers to submit thousands of domain-specific phrases, acronyms, and formatting preferences per transcription job or real-time stream.

If you share what kind of technical jargon you're dealing with (e.g., medical, legal, software engineering) and whether you need real-time or batch transcription , I can recommend which provider's approach fits your workflow best.

Yes. If the problem is technical jargon, product names, acronyms, or specialized terminology, several speech-to-text vendors offer ways to customize recognition:

What I'd choose

For your specific case—automated video captions that routinely mangle technical jargon—I'd start with Deepgram or Amazon Transcribe.

• If you have a manageable vocabulary list (say, hundreds of product names, acronyms, APIs, scientific terms, etc.), keyword/custom-vocabulary boosting may be enough. Deepgram explicitly supports keyword boosting for specialized terminology, while AWS provides custom vocabularies for domain-specific words and phrases.
• If the same terms need to be recognized correctly based on context, rather than simply recognized as individual words, look at actual custom model training. AWS's custom language models learn relationships between words, while Deepgram offers custom model training using audio and transcripts.
• Google's full custom model route is more demanding: its current documentation calls for at least 100 hours of training audio plus 10 hours of validation data, so I'd consider it when you have a substantial proprietary dataset rather than just a glossary.

One important distinction: "custom vocabulary" isn't necessarily model training. It can be a relatively lightweight list of terms that biases recognition. If your captions confuse contextually similar technical words—for example, consistently turning one specialized term into an ordinary English word—you'll likely get better results from a custom language/model-training approach.

If you tell me which STT/captioning system you're currently using (e.g. Whisper, Azure, Google, AWS, Deepgram, AssemblyAI, or a video platform), I can narrow this to the best 2–3 options and explain exactly how their customization works.

Yes. If the problem is technical jargon, product names, acronyms, or industry-specific terminology, several speech-to-text providers offer vocabulary customization rather than requiring you to train an entire model from scratch.

Deepgram is particularly explicit about the distinction: keyword boosting handles smaller sets of specialized terms, while its custom-model training can learn terminology, accents, and domain-specific language from your audio/transcript pairs.

AWS similarly supports custom vocabularies specifically for technical words, jargon, acronyms, brand names, and proper nouns, with optional pronunciation and display forms.

Microsoft offers a lighter-weight phrase list that can bias recognition toward your terms without training a model; its documentation recommends Custom Speech when you have a large vocabulary or need deeper adaptation.

What I'd choose for automated video captions

If you're processing prerecorded videos rather than doing live transcription, I'd shortlist Deepgram, AWS Transcribe, and Azure Speech and run a small benchmark using:

• 50–200 of your worst technical terms
• several hours of representative video
• the same audio through each provider
• term-level accuracy, not just overall WER

If you have existing corrected captions, that's especially valuable: providers that support custom model training can use the audio + corrected transcript pairs to learn your terminology rather than merely boosting a dictionary.

If you tell me what kind of technical jargon you're dealing with (software, medical, engineering, finance, etc.) and roughly how many videos/hours you process per month, I can narrow this to the 2–3 best options and compare their customization capabilities and pricing.

To fix inaccurate automated captions on technical jargon, brand names, or specialized acronyms, several major speech-to-text (STT) and voice AI providers offer custom vocabulary training, keyword boosting, or custom language models (CLMs).

The industry standard solutions fall into two categories: phrase boosting/custom vocabulary (quick lists of target words) and custom language models (deep training on domain text).

• Amazon Transcribe : Offers Custom Vocabulary (to supply discrete keyword lists and custom pronunciations) as well as Custom Language Models (CLMs) , which let you upload bulk domain-specific text data (like manuals or transcripts) so the model learns context rather than just isolated terms. Check the details on the AWS Amazon Transcribe Developer Guide.
• Microsoft Azure AI Speech : Provides a robust Custom Speech portal and API that lets you adapt and train acoustic and language models using your own text files and audio datasets to conquer specialized industry jargon. Learn more via Microsoft Azure Custom Speech Documentation.
• Google Cloud Speech-to-Text : Offers Model Adaptation and Custom Speech Models which allow you to boost the probability of specific domain terms, phrases, or classes (like alphanumeric product codes) during transcription. Explore implementation on the Google Cloud Custom Speech Documentation.
• AssemblyAI : Features a Word Boost API parameter that lets you inject up to 1,000 high-priority custom keywords or technical phrases to strongly bias recognition toward your specific lexicon. See how it works on the AssemblyAI Documentation.
• Deepgram : Uses Keyterm Prompting (custom vocabulary) within their Nova speech recognition models to let developers pass specialized words and real-time hints dynamically. Read about configuration on the Deepgram Documentation.
• Speechmatics : Features a Custom Dictionary supporting up to 1,000 words or phrases per job, complete with phonetic "sounds-like" spelling adaptations for unusual or non-standard technical terms. Details are available at Speechmatics.

If you can share whether you are processing pre-recorded video files in batch or needing real-time live captions , I can help you pick the best API provider and strategy for your workflow.

If your captions are struggling with technical jargon, product names, acronyms, or industry terminology, you generally have two categories of solutions:

1. Vocabulary adaptation (lightweight): You provide a glossary or keyword list that biases transcription toward specific terms.
2. Custom model training (deeper): The provider fine-tunes or adapts a speech model using your domain text and/or labeled audio, which can substantially improve accuracy for specialized language.

Here are some of the strongest options:

A few notes on the major platforms:

• Amazon Transcribe supports both custom vocabularies (for brand names, acronyms, and technical terms) and custom language models trained on domain-specific text, making it a strong choice if your terminology appears consistently across many videos.
• Google Cloud Speech-to-Text offers speech adaptation (phrase sets, custom classes, boosting) and newer Custom Speech models that can be trained using your organization's data.
• Azure AI Speech provides Custom Speech, which lets you improve recognition using your own text corpus and optionally transcribed audio recordings to better match your domain.

If you need a services partner

If you're looking for someone to do the work—building vocabularies, labeling data, evaluating accuracy, and deploying models—many AI consulting firms and speech AI specialists offer managed customization on top of AWS, Google Cloud, Azure, Speechmatics, or open-source models like Whisper. Typical engagements include:

• Building technical dictionaries and pronunciation lexicons
• Preparing and cleaning training transcripts
• Fine-tuning or adapting speech models
• Measuring word error rate (WER) before and after customization
• Ongoing vocabulary maintenance as products and terminology evolve

Which approach is likely right?

• A few hundred recurring technical terms: Start with a custom vocabulary or phrase boosting. It's inexpensive and often delivers a noticeable improvement.
• Highly specialized engineering, medical, legal, or scientific content: A custom speech model trained on representative text and audio usually produces the largest gains.
• Frequent new terminology: Choose a platform that supports easy vocabulary updates without retraining the entire model.

If you can share approximately:

• your industry (e.g. cybersecurity, biotech, software engineering),
• the number of video hours you process each month,
• and whether you need batch captions or live captions,

I can recommend the most suitable provider and estimate the level of improvement you can realistically expect.

Several major enterprise speech-to-text (STT) and artificial intelligence platforms offer custom vocabulary training, phrase boosting, or custom language model adaptation to accurately capture technical jargon, product names, and industry-specific acronyms.

Top Providers for Custom Vocabulary Training

• Deepgram : Offers a Key Term Prompting feature that lets you instantly pass custom vocabulary, brand names, and complex domain jargon into their Nova and Flux voice AI models without needing full retraining.
• AssemblyAI : Features Word Boost and Keyterms Prompting for both pre-recorded and real-time streaming APIs . This allows you to assign decode-time probabilities to specific rare technical words or phrases.
• Google Cloud Speech-to-Text : Uses a Model Adaptation feature (via Phrase Sets and Custom Classes) that lets you supply hint phrases and apply a positive boost value to bias the engine toward specialized or rare vocabulary.
• Amazon Transcribe : Supports both Custom Vocabularies (for discrete lists of words/acronyms) and Custom Language Models (CLMs) , which allow you to upload large text corpuses (like manuals or technical wiki pages) to train the model on broader domain context.
• AI-Media : Provides LEXI Text , an AI-powered live and recorded automated captioning toolkit that uses domain-specific Topic Models to recognize unusual terminology and technical jargon for broadcast and enterprise video workflows.

If you can share what kind of technical jargon you are working with (e.g., medical, legal, software engineering) and whether you need real-time streaming or batch file captioning , I can recommend the most cost-effective API configuration for your workflow.

To fix inaccurate automated captions for technical jargon, multiple major speech-to-text (STT) providers offer custom vocabulary training, phrase boosting , or Custom Language Models (CLMs) . These features let you feed the engine specialized domain terms, product names, acronyms, and uncommon proper nouns so it stops misinterpreting them.

Leading providers offering these customization capabilities include:

• Amazon Transcribe : Offers both custom vocabularies (to inject specific keywords/phrases) and Custom Language Models (CLMs), which allow you to train the underlying model on broad text data like manuals, wiki pages, or transcripts without needing matching audio.
• AssemblyAI : Uses keyterm prompting and custom vocabulary features to heavily bias the model toward recognizing specific domain-specific jargon, brand names, or technical terms.
• Google Cloud Speech-to-Text : Provides speech adaptation and model customization features, letting you boost specific phrases or adapt conformer-based base models with domain text and audio.
• Microsoft Azure AI Speech : Features a robust Custom Speech portal that lets you upload text files (utterances/jargon lists) and audio files to fine-tune a fully customized acoustic and language model.
• Rev AI : Supports a dedicated Custom Vocabulary API where you can submit thousands of domain-specific phrases and configure "sounds-like" spellings to ensure proper output for unique terms.
• Deepgram : Offers keyterm prompting features within its Nova models to dynamically steer the transcriber toward accurate spelling of specialized terminology.

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

• Are you looking for a real-time streaming solution or batch processing for pre-recorded videos?
• What specific technical field or jargon (e.g., medical, software engineering, legal) are the current models struggling with?

If your captions are failing on technical jargon, look for speech-to-text vendors that support custom vocabulary, phrase boosting, custom dictionaries, or domain adaptation. The main options include:

A practical approach is:

1. Create a terminology list
   Include:

   • product names
   • acronyms (“Kubernetes,” “OAuth,” “GraphQL”)
   • internal project names
   • scientific/legal/medical terms
   • preferred spellings and capitalization

2. Add pronunciations for uncommon words
   This helps when a term sounds like a common word.

3. Use domain text if available
   For example, feed documentation, manuals, transcripts, or knowledge-base articles into systems that support language-model adaptation. Amazon Transcribe, for example, distinguishes between simple vocabulary boosting and larger custom language models trained on domain text.

For automated video captions specifically, I’d shortlist:

• Amazon Transcribe if you have lots of technical terminology and can provide domain text.
• Google Cloud Speech-to-Text if you want flexible phrase boosting through APIs.
• Azure Speech if your stack is already Microsoft-based.
• Verbit if caption accuracy matters more than developer control.

If you tell me what kind of jargon you have (software engineering, medicine, finance, manufacturing, etc.) and whether you need live captions or post-production subtitles, I can narrow this down.