AI interpretation sounds so simple: someone speaks, the system translates, the other person hears it in their language. Organizations already trust AI for text translation every day, so it is easy to assume the same level of readiness applies to live conversation. Tools like Apple’s Live Translation make it seem so easy. And yet it is not. Interpretation asks AI to do something fundamentally harder than translation, and the quality of the experience depends entirely on how the provider builds, trains, and supports the system.
If you are looking to add AI interpretation to your language access program, this article walks through how we at BIG handle the engineering, the training, and the design decisions that separate a reliable, high-quality system from one that only works in a demo.
What It Takes to Get From “Hello” to “Hola”
When you use a text translation tool, a single model does all the lifting: text goes in, translated text comes out. From a user’s perspective, AI interpreting seems similar. But from a technical perspective, it’s much more complicated than that. It requires three distinct functions, each handled by a different technology, running in rapid sequence.
First, automatic speech recognition (ASR) listens to the speaker and converts audio into text in the source language. Then, machine translation (MT) translates that text into the target language. Finally, text-to-speech (TTS) generates natural-sounding audio so the other party hears spoken words in their language. Engineers call this a cascading pipeline.
Each of these steps is a separate model with its own strengths and limitations, but those additional two layers mean more engineering work and more potential failure points.
Where Real Conversations Get Difficult
Those 3 models work well under highly controlled conditions: clear audio, one speaker at a time, a standard dialect, and a steady speaking pace. Real conversations aren’t always like that (if ever).
Overlapping, mixed speech is one of the most common challenges. In a live call, the two parties sometimes talk over each other, or one person starts speaking before the system has finished processing the previous turn. We have addressed this partly through targeted data annotation: our team trains the ASR model to disregard interrupted speech in the other language and continue processing as though the interruption did not happen.
Background noise creates a similar problem, particularly in clinical and government settings where ambient sound can confuse both the speech recognition and the voice activity detection (VAD), the component that determines when a speaker has finished talking. Our roadmap includes confidence-level monitoring that detects when noise is degrading quality and prompts the user to adjust or switch to a human interpreter.
Accents and dialect variation add another dimension. Most ASR models perform best with standardized versions of a language. But the people who most need interpretation services often speak regional variants. Spanish spoken in Argentina sounds different from Spanish spoken in Mexico, and both sound different from European Spanish. Arabic varies widely across regions. We train for standardized versions first, then use targeted annotation and testing to extend coverage where our clients need it.
These are everyday conditions in healthcare, government, and contact centers. Simply put: An AI system that only works under ideal conditions is not fit for use in these settings.
How BIG Trains AI Interpretation Tools for Real-World Performance
Every AI interpretation system starts with pre-trained models that have a baseline level of quality. Getting from baseline to production-ready takes deliberate, client-specific work.
Our process starts with the client’s own data.
“When we take on a new account, we audit the calls that the client is already doing with our human interpretation services. We want to understand the domain, how their callers communicate, and what terminology matters most. From that analysis, we build glossaries and term bases, and then we do targeted data annotation to train the models. It is the quality and the attention to detail that sets us apart.”
— Maciej Modrzejewski, VP of Artificial Intelligence, BIG Language Solutions
A generic ASR model might have a word error rate of around 15%. With targeted training on audio that reflects your domain, your caller population, and your typical call conditions, we can bring that number below 10%. Every percentage point matters when accuracy determines whether a caller gets the right information.
Consistent terminology is critical. In healthcare, a medical term translated differently each time creates confusion and risk. Our glossaries make sure the system handles your key terms the same way every time. We also have a dedicated annotation team that tests the system daily, generating new training data and flagging quality issues before they reach your callers.
Then there is the low-resource language challenge. The languages with the least training data available are often the ones with the highest demand for AI interpretation. Languages like Karen, Hmong, and Somali can be difficult to staff with human interpreters, which makes AI coverage appealing.
But limited training data makes building accurate models harder. A study published in JAMA Pediatrics found that a general-purpose AI produced clinically significant errors in 23-33% of Haitian Creole translations, compared with 8% for professional human translators. For high-resource languages like Spanish, the gap was much smaller. That study only measured translation, one step of the pipeline, but it shows how fast quality drops for underrepresented languages when the models were not trained for them.
At BIG, we address this with synthetic data, training material generated and curated by other AI systems, to extend coverage where real-world data is scarce. Synthetic data is not a perfect substitute for large volumes of real-world audio, but it meaningfully improves accuracy for languages that would otherwise be underserved.
Balancing Speed and Accuracy
Latency matters. In a live conversation, every second of silence feels long. Yet, the models that produce the most accurate results tend to be the largest and slowest. Managing that trade-off is a real engineering challenge.
Keeping response times low without losing accuracy requires careful design. The models we use in production are smaller and faster than the ones used during training, built specifically for the kind of high-volume, low-latency processing that live conversation demands. This is also why we do not use large language models (LLMs) for the core interpretation pipeline. LLMs are powerful, but they are not built for real-time speed. We use specialized ASR, MT, and TTS models designed for fast response, with LLMs reserved for specific tasks, like glossary incorporation, where they are most helpful.
When evaluating interpretation systems, ask how fast the system responds and how often it makes mistakes. A system that is fast but inaccurate is not useful. A system that is accurate but keeps both parties waiting too long is not practical.
AI and Human Interpreters, Working Together to Deliver High Quality Experiences for the LEP Community
With approximately 26 million LEP individuals in the United States, demand for interpretation consistently outpaces the available supply of human interpreters, particularly for less common languages. AI interpretation helps close that gap. It is available instantly, with no scheduling delays and no hold times. It delivers consistent output across locations and teams. And it can provide coverage for hard-to-staff languages where organizations often struggle most to find qualified interpreters.
But not every conversation is right for AI. High-stakes interactions, situations that require cultural judgment, and conversations where emotional nuance matters all benefit from a trained human professional. The most practical approach treats AI and human interpretation as complementary. AI handles the easy calls, freeing up human interpreters to step in when things get complicated.
We designed our system around this principle. During any call, either party can switch to a live human interpreter without dropping the conversation. The system gives your organization the flexibility to match the right resource to the right conversation, every time.
What This Looks Like in Practice: BIG’s Real-Time AI Interpreter
This approach is reflected in BIG’s Real-Time AI Interpreter.
It delivers turn-based, speech-to-speech interpretation in 15+ languages, and we’re expanding into other languages, including for lower-resource languages where demand is highest. It is cloud-based and works across telephony, web, and InterpVault™, so it fits into the infrastructure your team already uses. It is encrypted end-to-end with HIPAA-aligned safeguards for organizations handling protected information.
For your callers, it means faster access to interpretation without scheduling delays, with consistent quality across every interaction. For your operations team, it means lower per-call costs, reliable availability for hard-to-staff languages, and full reporting and billing through InterpVault™. And for situations that truly need a human interpreter, the switch happens mid-call with no disruption.
If you are building or expanding a multilingual communication program, our team can walk you through how the Real-Time AI Interpreter fits alongside your existing interpretation services.
Ready to see how it works? Book a demo or talk to our team about your interpretation needs.
