Study: Measuring
AI Interpreting Accuracy
A real-world study of how Boostlingo tests AI interpreting in medically complex scenarios.
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In this 14-page study, we compared Boostlingo AI with leading open-source and commercial systems. Using real medical interpreting audio and a staged session, we scored 300+ utterances against expert translation references across three industry-standard metrics. The results show Boostlingo pulling ahead while setting a clearer evaluation standard. Get the study!
Why This Study Matters
AI interpreting is entering regulated industries faster than federal guidance can keep up, leaving organizations to set their own standards when evaluating tools and potential partners. This 14-page study, written by Senior Machine Learning Engineer Zac Bolton, provides a concrete way to do that.
It benchmarks two Boostlingo pipelines against a leading open-source model (Meta SeamlessM4T) and a top commercial system using real medical interpreting audio and a staged session between English and Spanish speakers. The result is a clear look at how these systems perform in the messy, high-stakes conditions that matter.
Testing with Real-World Audio
Clean, scripted sentences don’t fully capture the reality of medical conversations, often filled with interruptions, corrections, and background noise. To get as close as possible, we used two sources of data: anonymized, redacted medical interpreting audio and a staged session created by professional interpreters.
Three Industry Standard Metrics
After splitting the data into 308 utterances, we created expert reference translations and scored outputs using three industry-standard machine translation metrics: BLEU, COMETKiwi, and GEMBA. The correlation analysis shows these metrics largely move together, reducing the chance that any single score is driving the conclusion.
Boostlingo Leads Across Benchmarks
Overall mean scores place Boostlingo pipelines ahead of both SeamlessM4T and the commercial competitor, with Agent showing the largest separation: 87.3 vs 69.5 on GEMBA (overall mean).
Language Direction Matters
Spanish-to-English showed exceptional results. In ES→EN evaluations, Boostlingo Cascade outperforms SeamlessM4T, with the largest quality advantage showing in translation accuracy and fluency, scoring 72.7 on GEMBA, compared to SeamlessM4T’s 62.8.
Failure Examples Show Risk
We include real failure examples because numbers don’t show the full risk profile. You’ll see how even small errors can create big impacts in high-stakes environments like healthcare.
What Can I Do With This Study
Good decisions come from clear standards, not surface-level demos. When evaluating AI interpreting tools, focus on proof of accuracy rather than promises. Ask vendors how they test quality, what benchmarks they use, and how their results compare to other solutions. Look for evaluations grounded in real-world language use and measured across multiple quality metrics, not a single score or marketing claim.
This study provides a practical methodology you can use to evaluate vendors side by side, validate accuracy with confidence, and understand which AI interpreting solutions hold up in real conversations and which do not.
Want to See This Methodology Applied in Practice?
Explore Boostlingo’s AI Interpreter today.
