Why Expert Interview Transcripts Get Technical Terms Wrong

If you've ever reviewed an expert interview transcript and found "Jane and Tech" instead of "Genentech," you understand the frustration. Despite remarkable advances in speech recognition technology, specialized terminology remains a persistent blind spot.

This isn't a minor inconvenience. When the most important words in a conversation—the technical terms that carry actual insight—are the ones most likely to be wrong, the value of automated transcription comes into question.

The Domain Knowledge Gap

Modern automatic speech recognition (ASR) systems are trained on massive datasets of general language. They excel at transcribing everyday conversation, podcasts, meetings, and interviews about common topics. But expert interviews are fundamentally different.

When a pharmaceutical executive discusses checkpoint inhibitors, or a semiconductor analyst explains EUV lithography, the ASR system encounters sounds it wasn't trained to recognize. It makes its best guess—often wrong.

Real Examples from Expert Interviews

These aren't edge cases. They're the rule when dealing with specialized vocabulary that doesn't appear in general training data.

Why Speech Recognition Struggles

Understanding the technical reasons helps set realistic expectations.

Statistical Language Models

ASR systems work by matching audio patterns to probable words. When processing sound, they consider not just acoustic similarity but also what words typically follow each other in language.

The phrase "I work at Google" is easy—"Google" is common, and the context supports it. But "I work at Regeneron" presents a problem. The system has seen "Regeneron" far less frequently (if at all), so it reaches for acoustically similar alternatives from its vocabulary.

The Compounding Effect

Errors compound when multiple unfamiliar terms appear together. An oncology expert saying "We're combining atezolizumab with bevacizumab for EGFR-positive patients" contains four specialized terms in one sentence. The system struggles with each individually, and the lack of familiar context words makes recovery difficult.

Accent and Pronunciation Variation

Technical terms often have pronunciation variations even among experts. Is it "nivo-LU-mab" or "ni-VOL-u-mab"? These variations, combined with accents and speaking styles, create additional matching challenges for systems expecting consistent pronunciation patterns.

The 95% Accuracy Problem

Transcription services often advertise accuracy rates of 95% or higher. This sounds impressive until you consider where the errors concentrate.

General Words vs. Technical Terms

A 95% accuracy rate means roughly one error per twenty words. But errors don't distribute evenly. Common words like "the," "and," "we," and "they" are transcribed correctly nearly 100% of the time. The errors concentrate in:

• Proper nouns (company names, people)
• Technical terminology
• Industry-specific jargon
• Acronyms and abbreviations

These happen to be exactly the words that carry the most information in an expert interview.

The Information-Weighted View

Consider this excerpt from an oncology expert call:

"At [Company], we're seeing strong adoption of [Drug] for [Indication] patients, especially those with high [Biomarker] expression."

If the bracketed terms are wrong, the sentence is useless—or worse, misleading. The 95% accuracy rate that sounds acceptable becomes unacceptable when viewed through the lens of information value.

Impact on Research Workflows

Transcription errors create downstream problems that multiply their impact.

Search and Retrieval Failures

When "Genentech" appears as "Jane and Tech," "Janentech," and "Genen Tech" across different transcripts, searching for mentions of the company becomes unreliable. Analysts miss relevant content, or must search multiple variations hoping to catch everything.

AI Analysis Inheritance

Large language models and analysis tools that process transcripts inherit errors. A summary that mentions "nevalumab" instead of "nivolumab" perpetuates the mistake. Sentiment analysis on garbled company names produces meaningless results.

Credibility and Sharing

Sharing transcripts with colleagues, clients, or compliance teams is awkward when obvious errors appear throughout. Teams either spend time manually correcting transcripts (negating efficiency gains) or accept lower credibility.

Institutional Knowledge Degradation

Transcripts become part of institutional memory. Errors baked into archives mean future analysts working with historical transcripts face the same problems, unable to search reliably or trust the content.

Evaluating Transcription Quality

When assessing transcription solutions for expert interviews, standard accuracy metrics don't tell the full story.

Questions to Ask

Domain-specific testing: What accuracy does the system achieve on content similar to your actual use case? General benchmarks are less relevant than performance on your specific terminology.

Error distribution: Where do errors concentrate? A system with lower overall accuracy but better performance on proper nouns and technical terms may be more useful.

Vocabulary adaptation: Can the system be tuned for your domain? Solutions that accept custom vocabulary or learn from corrections improve over time.

Post-processing capabilities: What happens after initial transcription? Some systems include error correction stages that catch and fix common mistakes.

Red Flags

Watch for:

• Accuracy claims based only on general content
• No ability to customize for domain terminology
• Inability to provide sample transcripts from relevant domains
• Lack of transparency about error patterns

The Terminology Challenge by Industry

Different industries face varying degrees of transcription difficulty based on their vocabulary characteristics.

Pharmaceuticals and Biotechnology

Drug names are essentially random syllables to general ASR systems. Generic names (nivolumab, pembrolizumab, atezolizumab) follow naming conventions unfamiliar to general models. Brand names (Keytruda, Opdivo, Tecentriq) fare slightly better but still frequently error.

Financial Services

Ticker symbols, fund names, and financial instrument terminology create challenges. "NVIDIA" might become "in video" and "Berkshire Hathaway" rarely survives intact.

Technology

Product names, technical standards, and acronym-heavy discussion cause problems. "Kubernetes" has dozens of transcription variations, and "AWS EC2 instances" often becomes unrecognizable.

Healthcare and Medical

Medical terminology, drug interactions, and procedure names suffer the same issues as pharma, compounded by the critical nature of accuracy in clinical contexts.

Legal

Case citations, Latin terms, and precise legal language require accuracy that general transcription struggles to provide.

What Good Looks Like

Despite the challenges, accurate transcription of expert interviews is achievable. The key factors:

Pre-Interview Context

Systems that understand what terminology to expect before transcription begins perform dramatically better. Knowing an interview concerns oncology drug development allows the system to weight pharmaceutical terms appropriately.

Domain-Specific Training

Models trained on or fine-tuned for specific industries recognize terminology that general models miss. A system trained on thousands of hours of pharmaceutical content handles drug names far better than a general-purpose alternative.

Post-Transcription Correction

Intelligent post-processing that reviews transcripts against expected terminology catches errors that initial transcription missed. This is especially effective when combined with interview context.

Continuous Learning

Systems that learn from corrections and user feedback improve over time for specific domains and terminology.

Setting Realistic Expectations

Perfect transcription of highly technical content remains challenging even for human transcriptionists unfamiliar with a domain. Realistic expectations for automated systems:

Achievable now: High accuracy on general discussion, speaker identification, timestamps, and formatting. Reasonable accuracy on common technical terms with appropriate preparation.

Improving rapidly: Domain-specific accuracy with customization, proper noun handling, and context-aware correction.

Still challenging: Novel terminology, heavily accented speech, poor audio quality, and extremely specialized jargon.

The gap between current capability and research team needs is closing, but hasn't disappeared.

Making the Most of Current Technology

While waiting for perfect transcription:

Prepare terminology lists in advance

Identify key terms, companies, and names likely to appear. Systems that accept custom vocabulary benefit from this preparation.

Review strategically

Focus review time on sections with technical content rather than attempting to correct entire transcripts.

Standardize corrections

When you do correct errors, do so consistently to build searchable archives.

Provide feedback

Systems that learn from corrections improve. The effort of providing feedback pays dividends over time.

Consider the full workflow

Transcription is one step in a research workflow. Evaluate solutions based on total workflow impact, not just raw transcription accuracy.

Looking Forward

Speech recognition technology continues to advance rapidly. Techniques that seemed experimental recently—domain adaptation, context-aware processing, and large language model integration—are becoming standard.

The organizations that understand transcription limitations today and work actively to address them will be best positioned to benefit as technology improves. Those that ignore the problem or accept poor quality as inevitable fall further behind.

Expert interviews are conducted specifically to capture specialized knowledge. Transcription that fails on specialized terminology defeats the purpose. Demanding better—and understanding what better looks like—is the first step toward getting it.

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InsightAgent is building transcription technology designed specifically for expert research. Learn more.