How accurate are AI transcripts for technical or medical terms?

A cardiologist dictates "Start metoprolol 25 mg twice daily" into an ambient scribe. The transcript reads "Start metoclopramide 250 mg twice daily." One's a beta-blocker for heart failure. The other's an anti-nausea drug at ten times the intended dose. Two words changed, and you've got a completely different medication at a dangerous dosage sitting in a patient's chart.

This isn't hypothetical. Medication errors are the most frequent and avoidable source of patient harm in healthcare, and transcription mistakes are a direct contributor. But the problem isn't limited to medicine. Legal teams deal with mangled case citations. Engineers watch product names get butchered in meeting transcripts. Contact centers lose critical account numbers to misrecognition.

The accuracy of AI transcripts on technical and domain-specific terminology is what separates a useful tool from a liability. This article breaks down why specialized terms are so hard for speech-to-text models, how to actually measure accuracy in ways that matter, and the specific tools and techniques you can use to get transcripts right on the terminology that counts.

Why technical and medical terms are hard for speech-to-text

Standard speech-to-text models are trained on general language — conversations, podcasts, meetings, news broadcasts. They're optimized for the words people say most often. But technical and medical vocabulary lives in a completely different distribution. These terms are rare in general training data, phonetically complex, and full of ambiguity that only domain context can resolve.

Here's what makes them so challenging.

Similar-sounding terminology

Medical vocabulary is packed with near-homophones that mean completely different things. "Metoprolol" (a beta-blocker for heart conditions) and "metoclopramide" (an anti-nausea medication) sound almost identical when spoken quickly. "Celebrex" (an anti-inflammatory) and "Celexa" (an antidepressant) are one syllable apart. "Lamictal" (for seizures) and "Lamisil" (for fungal infections) could easily be swapped by a model that hasn't learned the clinical context.

Drug names are particularly treacherous because every medication has at least two names — brand and generic — and many have common abbreviations on top of that. A single medication might be referred to as "acetaminophen," "Tylenol," or "APAP" in the same conversation.

Abbreviations with multiple meanings

"PT" means physical therapy in an orthopedic note and prothrombin time in a lab report. "MS" could be multiple sclerosis, morphine sulfate, or mitral stenosis. "CA" might refer to cancer, calcium, or cardiac arrest depending on the specialty. Without understanding the clinical context, an AI model has no way to expand these abbreviations correctly — or even know whether to expand them at all.

This problem extends well beyond medicine. In software engineering, "GC" could mean garbage collection or Google Cloud. In legal proceedings, "motion" has a specific procedural meaning that general models might not preserve. Domain abbreviations are everywhere, and they're inherently ambiguous without context.

Rapid dictation and environmental noise

Clinicians don't dictate like news anchors. They speak fast, often while multitasking — walking between patient rooms, reviewing charts, or performing procedures. The speech patterns are clipped, full of self-corrections, and loaded with jargon that comes out in rapid bursts. Add to that the background noise of a busy clinical environment — beeping monitors, overlapping conversations, equipment sounds — and you've got audio conditions that push any model to its limits.

Technical meetings have their own version of this problem. Engineers talking over each other about system architecture, rattling off API names and version numbers, switching between code terminology and plain English mid-sentence. The combination of speed, noise, and vocabulary complexity creates a perfect storm for transcription errors.

General models aren't built for this

The thing is, most ASR models are optimized to minimize overall Word Error Rate across general-purpose audio. They learn statistical patterns from large datasets of conversational speech. A word like "metformin" might appear thousands of times less frequently than "met for men" in general training data, so the model defaults to the more statistically likely interpretation. The model isn't wrong from a probability standpoint — it just doesn't have the domain knowledge to know that "met for men" makes no sense in a clinical context.

This is why specialized approaches matter. General accuracy and domain accuracy are fundamentally different problems.

How to measure accuracy on specialized terms

Before you can improve accuracy on technical vocabulary, you need to know how to measure it properly. And the standard metric most vendors advertise — Word Error Rate — doesn't tell you what you actually need to know.

The problem with Word Error Rate

Word Error Rate (WER) calculates the percentage of words that are wrong in a transcript: (substitutions + deletions + insertions) / total words x 100. A transcript with 5% WER sounds impressive — 95 out of every 100 words are correct. But WER treats every word equally. Missing the word "um" gets the same penalty as changing "15 mg" to "50 mg."

Consider this: a transcript could score 98% on WER while containing a single error that changes "no known allergies" to "known allergies." That's one deleted word out of hundreds, barely a blip in the WER calculation, but it's a potentially fatal mistake in an emergency room.

WER is useful for comparing overall model quality, but it's a poor proxy for clinical or domain accuracy. You need metrics that weight errors by their actual impact.

Missed Entity Rate: a better measure for domain accuracy

Missed Entity Rate (MER) specifically measures how often a model fails to correctly transcribe named entities — drug names, dosages, proper nouns, technical terms, and other domain-critical vocabulary. This metric focuses on the words that actually matter for downstream decision-making.

So when you're evaluating medical speech-to-text software for technical or medical use cases, MER gives you a much clearer picture than WER alone. A model with slightly higher WER but significantly lower MER is almost always the better choice for domain applications.

How the models actually compare

AssemblyAI's Universal-3 Pro with Medical Mode delivers a 3.2% Missed Entity Rate on medical terminology, compared to Deepgram Nova-3 Medical at 8.7% and AWS Transcribe Medical at 24.4%. On Word Error Rate for medical audio, the numbers are 5.3% for AssemblyAI versus 5.9% for Deepgram and 12.9% for AWS.

These benchmarks matter because they're measured on real clinical audio, not cherry-picked samples. The gap between providers on medical terminology accuracy is substantial — nearly 3x between AssemblyAI and AWS on MER. When you're building a product where medication names and dosages need to be right, that difference is the entire product.

For non-medical technical domains, the picture is similar. Universal-3 Pro achieves the lowest missed entity rates across categories including names, locations, organizations, emails, URLs, and phone numbers when compared against Amazon Transcribe, Deepgram Nova 3, ElevenLabs Scribe 2, Microsoft Azure, and OpenAI GPT-4o Transcribe.

Tools for improving accuracy on domain-specific terms

Knowing the problem exists is step one. Here's how to actually fix it. AssemblyAI provides several features specifically designed to improve transcription accuracy on technical and domain-specific vocabulary. Each one targets a different aspect of the problem, and they can be combined for maximum effect.

Medical Mode

Medical Mode is a purpose-built add-on that enhances transcription accuracy for medical terminology — medication names, procedures, conditions, and dosages. It reduces missed medical entities by over 20% compared to Universal-3 Pro alone, and it's optimized specifically for medical entity recognition to correct terms that general models frequently get wrong.

You enable it by setting a single parameter: domain="medical-v1". No changes to your existing pipeline are required.

Medical Mode supports English, Spanish, German, and French, and works with all of AssemblyAI's pre-recorded and streaming speech-to-text models. It's billed as a separate add-on at $0.15/hr.

import assemblyai as aai

aai.settings.api_key = ""

audio_file = "https://assembly.ai/lispro"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    domain="medical-v1",
)

transcript = aai.Transcriber().transcribe(audio_file, config)

print(transcript.text)

The difference is immediately visible. Here's a real before-and-after example:

Without Medical Mode:

I have here insulin to be used for both prandial mealtime and sliding scale is — insulin lisprohumalog subcutaneously.

With Medical Mode:

I have here insulin to be used for both prandial mealtime and sliding scale is — insulin Lispro (Humalog) subcutaneously.

Medical Mode correctly formats the output following standard medical convention — generic name first, brand name in parentheses. That's not just cosmetic. It's the format clinicians expect and the format that reduces downstream errors in clinical documentation systems.

Keyterms prompting

Keyterms prompting lets you provide up to 1,000 words or phrases (maximum 6 words per phrase) to improve transcription accuracy for those specific terms and related variations. This is your go-to tool when you know which domain-specific words matter most for your use case.

The key insight: you don't need to list every possible term. Keyterms prompting doesn't just match exact strings — it helps the model understand the semantic context around those terms, improving recognition of related terminology and contextually similar phrases as well.

Start with no keyterms and add terms based on words you consistently see the model struggle with. Including too many common terms that are already well-represented in the training data can lead to overcorrections.

import assemblyai as aai

aai.settings.api_key = ""

audio_file = "https://assembly.ai/wildfires.mp3"

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
)
config.set_custom_spelling(
  {
    "Gettleman": ["gettleman"],
    "SQL": ["Sequel"],
  }
)

transcript = aai.Transcriber(config=config).transcribe(audio_file)

if transcript.status == "error":
  raise RuntimeError(f"Transcription failed: {transcript.error}")

print(transcript.text)

This approach is particularly effective for proper nouns with unusual spellings, company-specific terminology, product names, and technical abbreviations that have domain-specific meanings.

Prompting with Universal-3 Pro

Universal-3 Pro is a Speech-augmented Large Language Model (SpeechLLM) — which means it responds to natural language prompts that guide how it transcribes. You can use the prompt parameter to improve entity accuracy and provide domain context.

For improving accuracy on technical terminology across any domain, use a prompt that describes the pattern of entities you want corrected:

Use standard spelling and the most contextually correct spelling of allwords including names, brands, drug names, medical terms, and proper nouns.

For providing domain-specific context that helps the model make better decisions about ambiguous terms, pair that with a context clue:

This is a doctor-patient visit. Prioritize accurately transcribingmedications and diseases wherever possible.

But here's where it gets interesting: context alone doesn't tell the model how to transcribe. "This is a doctor-patient visit" is context. "Prioritize accurately transcribing medications and diseases" is the actionable instruction. You need both. The context sets the domain; the instruction tells the model what to prioritize within that domain.

A few important prompting principles to keep in mind:

• Describe the pattern of entities you want corrected, not specific errors — listing exact spellings often causes the model to hallucinate them
• If you know the exact terms you need, use keyterms prompting rather than describing them in a free-form prompt
• Start with the default prompt (which is already optimized for accuracy) and add one instruction at a time
• Use authoritative language — "Required:", "Mandatory:", and "Always:" get higher compliance than softer phrasing

Combining features for maximum accuracy

These features aren't mutually exclusive. For the highest possible accuracy on medical or technical audio, combine Medical Mode, keyterms prompting, and speaker diarization in a single configuration:

config = aai.TranscriptionConfig(
    speech_models=["universal-3-pro", "universal-2"],
    language_detection=True,
    domain="medical-v1",
    speaker_labels=True,
    keyterms_prompt=["Lisinopril", "Metformin", "Humalog"],
)

This configuration gives you Medical Mode for broad medical entity recognition, keyterms prompting for specific drugs or terms unique to your use case, and speaker diarization to correctly attribute who said what — critical in clinical conversations where the difference between a patient reporting a symptom and a doctor noting a finding completely changes the medical meaning.

For streaming applications, the same combination works. You can even update keyterms dynamically mid-stream as the conversation progresses — for example, switching from scheduling-related terms to clinical terms when a voice agent moves from appointment booking to a medical intake stage.

What about non-English languages?

Technical accuracy challenges get amplified when you add language diversity into the mix. Many speech-to-text providers see significant accuracy drops on non-English audio, especially for domain-specific terminology that may not appear frequently in multilingual training data.

Universal-3 Pro supports English, Spanish, Portuguese, French, German, and Italian natively with code-switching — meaning it can handle audio where speakers switch between languages mid-conversation without requiring separate model configurations. For access to all 99 supported languages, use "speech_models": ["universal-3-pro", "universal-2"], which falls back to Universal-2 for languages Universal-3 Pro doesn't yet cover.

Medical Mode specifically supports English, Spanish, German, and French for medical terminology enhancement. If you use Medical Mode with an unsupported language, the API ignores the domain parameter gracefully — your transcript is still returned using standard transcription, and you won't be charged for Medical Mode.

For improving transcript accuracy on non-English technical content, the same strategies apply: use keyterms prompting for domain-specific terms in the target language, and use prompting to provide language-specific context. You can even prepend "Transcribe [language]" to your prompt to guide the model toward a specific language when you know it in advance.

How to improve accuracy on poor-quality audio

Even the best model can only work with the audio it receives. Poor recording conditions — compressed phone audio, background noise, far-field microphones, overlapping speakers — degrade accuracy on all vocabulary, but technical terms suffer disproportionately because they're already at the edge of the model's confidence.

A few practical strategies:

• Invest in input quality: High-quality microphones and noise-canceling technology make a measurable difference. For medical dictation workflows, this is one of the highest-ROI investments you can make.
• Use keyterms prompting aggressively: When audio quality is poor, giving the model explicit guidance about which terms to expect helps it resolve ambiguous acoustic signals in favor of the correct domain terms.
• Adjust silence thresholds for medical audio: Clinical conversations have different speech patterns than typical voice interactions. Doctors pause to think, review charts, or formulate diagnoses. Increasing silence thresholds (e.g., min_turn_silence: 800, max_turn_silence: 3600) prevents the model from fragmenting these natural pauses into separate turns, which can break context and reduce accuracy.
• Combine multiple accuracy features: Medical Mode + keyterms + prompting together provide more resilience against poor audio than any single feature alone, because each feature addresses a different source of error.

Real-world applications

The techniques we've covered aren't theoretical. Here's how they play out across industries that depend on accurate transcription of specialized vocabulary.

Medical scribes and clinical documentation

Ambient clinical documentation is the fastest-growing application for medical speech-to-text. AI scribes listen during patient encounters and generate structured clinical notes — SOAP notes, discharge summaries, referral letters. The accuracy requirements are the highest in any industry because errors directly affect patient care.

Medication names and dosages are the critical path. Getting "Ramipril 5 mg daily" right is what makes the note usable. Getting it wrong creates a documentation error that follows the patient through their entire care journey. Building an AI medical scribe that clinicians trust requires Medical Mode combined with keyterms prompting for a practice's common formulary.

Legal transcription

Legal proceedings have their own specialized vocabulary — case citations like "Duran v. Peabody Coal Company," Latin terms like "amicus curiae" and "voir dire," and procedural language like "motion for summary judgment" that has precise legal meaning. A deposition transcript that mangles case citations is useless for legal research.

Keyterms prompting is the primary tool here. Legal teams can provide the specific case names, legal terms, and proper nouns they expect to appear, and the model adjusts its recognition accordingly.

Technical meetings and engineering discussions

Product names, API endpoints, version numbers, acronyms — engineering conversations are dense with terminology that general models struggle with. "We need to migrate the CloudGuard SSO integration to v3.2" is the kind of sentence where every technical term matters and none of them appear in general conversational training data.

Custom spelling lets you enforce exact formatting for your product vocabulary — ensuring "CloudGuard" stays as "CloudGuard" instead of becoming "cloud guard" or "Cloudguard." Keyterms prompting handles the broader technical vocabulary.

Contact centers

Contact centers process thousands of calls daily, and the critical information is often the most domain-specific: account numbers, product names, company-specific terminology, policy references. When a customer says their policy number or a specific product name, that entity needs to be captured exactly right for downstream analytics, compliance monitoring, and automated workflows to function. Effective conversation intelligence depends on getting these entities right.

The combination of keyterms prompting (for company-specific terms) and dynamic mid-stream updates (adjusting terms as the call progresses through different stages) gives contact center applications the flexibility to maintain high accuracy across diverse call types.

Looking forward

The gap between AI transcription and human transcription for domain-specific terminology is closing fast — but it's closing because of purpose-built features, not because general models are magically getting better at rare vocabulary. Medical Mode, keyterms prompting, and SpeechLLM prompting represent a fundamentally different approach than trying to train a single model on everything.

What's changing is that these specialized capabilities are becoming easier to access. A few years ago, getting clinical-grade transcription accuracy meant building custom models, maintaining specialized vocabularies, and running expensive infrastructure. Now it's a single parameter: domain="medical-v1". The complexity is moving from the developer's plate into the platform.

For teams building products that depend on accurate transcription of specialized vocabulary — whether that's medication names, legal citations, or engineering jargon — the most important decision isn't which model has the best overall WER. It's whether your speech-to-text provider gives you the tools to optimize for the specific terms that matter in your domain.

The accuracy is there. The tools exist. The question is whether you're using them.

Frequently asked questions

Which speech-to-text API has the highest accuracy for technical terminology?

AssemblyAI's Universal-3 Pro delivers the lowest Word Error Rate on English audio at 5.9% and achieves the best entity recognition accuracy across categories including names, locations, medical terms, emails, URLs, and phone numbers. For medical terminology specifically, Universal-3 Pro with Medical Mode achieves a 3.2% Missed Entity Rate — compared to 8.7% for Deepgram Nova-3 Medical and 24.4% for AWS Transcribe Medical. Keyterms prompting lets you boost accuracy for up to 1,000 domain-specific terms per request.

How do I improve transcript accuracy for poor-quality audio?

Start with the highest quality input you can get — invest in good microphones and noise-canceling technology. Then layer AssemblyAI's accuracy features: use keyterms prompting to give the model guidance on which domain terms to expect, enable Medical Mode if you're working with clinical audio, and use prompting to provide context about the audio domain. For streaming medical audio, increase silence thresholds to prevent premature turn boundaries that break context. Combining multiple features provides more resilience against poor audio than any single approach.

How does AssemblyAI compare to Deepgram for medical transcription accuracy?

On medical entity recognition, AssemblyAI Universal-3 Pro with Medical Mode achieves a 3.2% Missed Entity Rate versus Deepgram Nova-3 Medical at 8.7% — meaning AssemblyAI misses significantly fewer medication names, dosages, and clinical terms. On Word Error Rate for medical audio, AssemblyAI delivers 5.3% versus Deepgram's 5.9%. Medical Mode is available for both pre-recorded and streaming transcription, supports four languages, and combines with keyterms prompting and speaker diarization for clinical documentation workflows. AssemblyAI offers a Business Associate Agreement (BAA) for customers who need to process Protected Health Information (PHI).

How accurate is AI transcription for non-English languages?

Universal-3 Pro supports six languages natively (English, Spanish, Portuguese, French, German, Italian) with code-switching, meaning it handles multilingual audio where speakers switch languages mid-conversation. For broader language coverage, using "speech_models": ["universal-3-pro", "universal-2"] provides access to 99 languages. Medical Mode supports English, Spanish, German, and French for medical terminology. For non-English technical content, keyterms prompting works across all supported languages to boost recognition of domain-specific terms.

How do I inject custom vocabulary or domain-specific terms in transcripts?

AssemblyAI provides three approaches. Keyterms prompting lets you pass up to 1,000 domain terms that the model prioritizes during transcription — this is the most effective method for boosting recognition of specific words. Custom spelling uses a find-and-replace approach to enforce exact formatting of terms in the final transcript (e.g., ensuring "SQL" renders as "Sequel"). Prompting with Universal-3 Pro provides natural language instructions that set domain context and guide transcription style. For maximum accuracy, combine keyterms with Medical Mode or prompting rather than relying on any single feature.

Does AssemblyAI support HIPAA requirements for medical transcription?

AssemblyAI enables covered entities and their business associates subject to HIPAA to use AssemblyAI services to process protected health information (PHI). AssemblyAI offers a Business Associate Agreement (BAA) and is SOC 2 Type 2, ISO 27001:2022, and PCI DSS v4.0 certified. Medical Mode does not change existing data handling or retention policies. For BAA setup or enterprise pricing, contact the AssemblyAI sales team.