Evaluating ASR Systems, Part 1: The Big Picture

A practical guide for teams integrating on-device ASR

Author: Ognjen Todic | November 11, 2025

In speech recognition, and tech in general, bold claims are everywhere:

“Best accuracy.”

“Diverse training data.”

“Real-world performance.”

They sound great, but too often, there’s little transparency or data behind them. Without metrics, it’s impossible to know whether a claim reflects real capability or just clever marketing.

It reminds me of a story a friend once told me. She used to travel constantly for work and always struggled to keep up with laundry. One weekend, she spotted a dry cleaner with a big sign that read “Same Day Service!” Relieved, she dropped off her clothes and asked when she could pick them up.

“Tomorrow,” said the attendant.
“But what about same-day service?” she asked.
He shrugged: “Oh, same-day service... Advertisement Only.”

That story always stuck with me, because it’s exactly how many technology claims sound; great on paper, but meaningless without data.

Without transparency and without metrics, claims about how something is done (rather than how well it works) are often just another “Same Day Service!” sign in the window. Advertisement Only.

Why Evaluating ASR Isn’t Straightforward

At Keen Research, our focus is on building Software Development Kits for on-device speech recognition. When we talk with teams exploring our solutions, one of the first questions we hear is:

“How well does it work?”

It’s a fair question, and a surprisingly complex one.

In this post, I’ll outline how to think about evaluating ASR systems, not just in terms of accuracy, but across all the dimensions that determine real-world performance.

Evaluating ASR systems sounds simple: run audio file through the speech recognition system, compare it to the reference transcript, and get a number. But that number can tell the wrong story if you’re measuring the wrong thing, or testing on data that looks nothing like your real-world use case.

Real-world performance depends on many factors:

Who’s speaking: adults, children, non-native speakers, regional accents.
Where they’re speaking: quiet rooms, classrooms, cars, crowded stores.
How the audio is captured: microphones, headsets, sampling rates, devices.
Where the model runs: on-device or in the cloud, each with its own constraints.
How the ASR result is used: whether it drives a command, fills a form, powers a transcription, or supports pronunciation feedback; each use case may (and often should) rely on a different metric for accuracy.

The Many Dimensions of ASR Evaluation

When people think about ASR performance, they often jump straight to accuracy. But accuracy is only one part of a much broader evaluation framework. Real-world performance depends on several interconnected dimensions; each affecting user experience, cost, and reliability in different ways.

Accuracy and Robustness

How consistently does the system recognize speech across different speakers, accents, and environments?

Accuracy measures correctness, while robustness shows how that accuracy holds up under real-world variability like noise, children’s voices, or variety of microphones/devices.

Latency and Responsiveness

How quickly does the system produce usable results?

A solution that’s slightly less accurate but noticeably faster can feel better to users, especially in interactive or educational settings.

Computational Efficiency and Cost

How much CPU, memory, and battery does the system consume? And, who pays for it?

For on-device ASR, efficiency determines whether it can run locally in real time or operate for extended periods in always-on listening scenarios without draining the device. For cloud systems, it drives scalability and operational cost, affecting how well the service supports large numbers of concurrent users.

Privacy and Security

Speech data often carries personal or sensitive content.

On-device ASR keeps audio local, offering greater privacy and compliance benefits. Cloud ASR enables central updates and monitoring, but requires careful data handling and user-trust safeguards.

Deployment, Maintainability, and Reliability

Beyond runtime performance, it’s important to consider how easily your overall system – the product or service that uses ASR – can be deployed and maintained.

How many potential points of failure exist? Servers, APIs, or internet connectivity? Can your product continue working when offline or under limited conditions (e.g. a flaky internet connection)? The fewer dependencies ASR solution introduces to your system, the more predictable and resilient it tends to be.

Bringing It All Together

There’s no single “best” ASR system; only one that fits your context. A model that’s slightly less accurate but faster, privacy-preserving, and easier to maintain may deliver a far better overall experience.

Some evaluation dimensions are straightforward. If your users don’t have reliable internet connectivity, cloud ASR isn’t the right fit; you don’t need a benchmark to know that. Others, like accuracy, latency, or robustness, might take a bit more exploration.

For simple or well-defined use cases, quick hands-on testing can be enough to build confidence. But as scenarios become less predictable – or start to deviate from well-understood use cases – meaningful evaluation often requires a more systematic, iterative approach.

That’s where having a structured evaluation process really helps.

The Iterative Nature of Evaluation

Some aspects of ASR evaluation aren’t one-time tasks; they’re ongoing cycles that deepen both your setup and your understanding of how the system performs in the real world.

You don’t just measure accuracy once and call it done; you use each evaluation cycle to learn where and why your ASR system succeeds or fails (or if your reference data is not accurate), and then improve. A typical loop looks like this:

Collect or refine data.
Evaluate using relevant metrics.
Analyze results to identify weaknesses, edge cases, or systemic patterns. These insights can guide not only configuration changes or model improvements but also product design decisions
Adjust datasets or models and their configurations.
Re-test and compare.

You don’t need a perfect dataset or a full research setup to start.

What matters is that your process helps you learn where the system works well and where it doesn’t, so you can make informed choices, whether that means tuning a configuration, or redesigning an interaction.

At Keen Research, we often work with prospects to help them understand which aspects of evaluation matter most for their use case, whether that’s accuracy, latency, or on-device efficiency. In future posts, we’ll share more about how to structure that process and set up an efficient evaluation workflow.

Evaluate with Engineering Rigor

You don’t need academic-level research methods to evaluate ASR effectively; but you do need a strong engineering approach.

For simple or well-understood use cases, quick hands-on testing can be enough to build confidence. As your scenarios become broader or start to deviate from well-understood use cases, a more systemic evaluation helps ensure that your system performs reliably in real-world conditions.

The goal isn’t to build a research lab; it’s to gather enough evidence to trust your system and make informed decisions.

Transparency, Metrics, and “Advertisement Only”

We’ve come full circle.

The difference between a bold claim and a credible statement often comes down to transparency. Without clarity on how something was evaluated – what data, what conditions, what metrics – claims like “best accuracy” or “trained on diverse data” don’t mean much.

They’re just “Same Day Service!” signs in the window. Advertisement Only.

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