The progress in automatic speech recognition (ASR) over the past decade has been remarkable. Today, it powers everything from voice assistants and live transcription tools, to customer service platforms and accessibility solutions.
But as powerful as ASR is, it’s far from perfect.
But as powerful as ASR is, it’s far from perfect.
And ASR systems particularly struggle with language nuances and variations. These include regional accents, dialects, sociolects, and non-dominant languages.
This inherent bias is both a technical problem, and a human one. And for companies building products on top of ASR, it can have real consequences: poor customer experiences, reduced accessibility, and reputational risk.
This article explores the causes of ASR language bias in ASR, how it affects businesses and users, and how AI technologies create more inclusive, accurate, and responsible speech recognition.
Key takeaways:
Language bias in ASR refers to measurable performance issues when dealing with different speakers and speech styles. ASR systems are typically much better at understanding some people than others based on how closely their speech matches the system’s training data.
Most ASR systems today are built on large datasets dominated by a very specific subset of speech: well-articulated, standard U.S. English.
According to research, around 92.65% of GPT-3's training tokens are in English. LLaMA 2 isn’t far off, at 89.70% English. And all other languages combined make up just a fraction of the data.
These systems naturally perform best with speakers who sound like the people most represented in the training data—typically white, male, American English speakers. On the flip side, they struggle to accurately recognize and transcribe other speakers.
While many models are multilingual, they often treat non-English languages through an English-first lens, leading to weaker, less natural performance.
What does this look like in real-world ASR use? It means that:
This is language bias—and in many real-world contexts, it results in broken user experiences, exclusion, or worse.
Language bias is both observable and measurable using word error rate (WER)—the percentage of words a system transcribes incorrectly. A lower WER means better performance.
While human transcription typically hovers around 4% WER, ASR tools routinely exceed this, especially when dealing with diverse speech inputs. And crucially, the disparity in WER isn’t consistent across speakers.
Recent research into ASR language bias shows:
For companies using speech-to-text tools, accuracy is table stakes. But so is speed and scalability. Users want the fastest possible tool that also makes the fewest possible mistakes.
From product quality to global growth and brand reputation, here are the key reasons this issue deserves attention.
Fully automated voice agents route calls, answer queries, or triage support tickets. But if the system struggles with accents, code-switching, or diverse English variants, interactions break down.
If your voice agent can’t understand the customer, it can’t help the customer.
Even when ASR is used to support human agents, risks remain. Misleading transcripts, faulty call summaries, and inaccurate action items erode trust in the system. If your ASR outputs are flawed, your productivity tools become liabilities—not accelerators.
Emerging markets represent massive growth potential. But they also come with complex linguistic realities.
Take India, for example. It has more than 20 officially recognized languages, hundreds of dialects, and a population that’s mobile-first and tech-savvy. But ASR systems trained on U.S. English or other dominant languages falter here. They fail to handle local accents, hybridized speech, or language switching.
For companies selling into global markets, this is a lost opportunity.
Accuracy is the most visible benchmark for ASR tools. Buyers might tolerate some latency or limited features, but they won’t overlook obvious and constant transcription errors.
Failures are hard to hide. A system might perform flawlessly in demos or with internal test data, only to fall apart in real-world customer calls.
When users spot inconsistencies, trust declines. Fast but unreliable tools may tick boxes on paper, but their value erodes as the errors pile up.
In a crowded market, accuracy is a key differentiator—and bias is one of the main threats to that accuracy.
As mentioned above, marginalized groups are disproportionately affected by ASR failures. For enterprise buyers, this brings a real reputational risk. Companies are expected to deliver inclusive, frictionless customer experiences. If the tech they use creates bias or exclusion, that reflects poorly on them and erodes confidence in their tech vendors.
No decision maker wants to explain to their board why their voice agent can’t understand a significant portion of their customer base. And no CCaaS provider wants to lose customers because of reputational damage caused by their technology.
There’s a broader societal cost to language bias in ASR: the widespread adoption of automated tools could accelerate the erosion of linguistic diversity.
Globally, there are more than 7,000 living languages, but 42% are considered endangered.
ASR systems—like many AI tools—reinforce dominant languages, especially English. When voice interfaces can only understand the “mainstream,” minority languages and dialects become less visible, less used, and eventually, less viable.
The more AI systems prioritize dominant speech patterns, the more they contribute to the marginalization of others. But the opposite is also true: technology companies have a real opportunity to preserve and elevate linguistic diversity by building inclusive, multilingual support systems.
ASR language bias isn’t inevitable. It’s not a hard-and-fast flaw of artificial intelligence or large language models (LLMs), it’s a reflection of the data and design choices behind them.
In fact, when developed thoughtfully, AI can actively reduce bias rather than reinforce it. Here are just three of the many ways technology can help.
Inclusive ASR is a commercial opportunity. By building systems that accurately understand a wider range of speakers, platforms can serve more users in more markets with higher reliability.
That means better customer engagement, fewer transcription mistakes, and smoother experiences in voice-based services like customer support, virtual assistants, and call analytics.
AI gives us a chance to build systems that are not only scalable, but also more consistent and more linguistically inclusive than traditional human support ever was.
Tailoring the models makes a big difference. In one study, the word error rate for Nigerian-accented English dropped from 44.2% using Google’s STT API to just 8.2% using a purpose-built, accent-aware model. Proof that specificity leads to performance gains.
ASR systems become more inclusive when they’re built on better, more diverse data—and when that data is treated with care. With balanced training sets, ethical development practices, and deliberate representation of underrepresented voices, AI can help support diversity rather than suppress it.
Nearly half of the languages spoken today are endangered. Most of these have limited digital presence, and very few are supported by commercial voice tools. But AI offers a path forward.
When ASR is developed with linguistic diversity in mind, it can document rare speech patterns, make minority languages usable in digital environments, and support community efforts to keep them alive. Voice technologies that embrace diversity aren’t just more accurate—they’re also more culturally meaningful.
By expanding the linguistic scope of AI systems, we don’t just build better products—we help protect a crucial part of human heritage.
Bias in ASR isn’t solved by chance—it takes deliberate design, diverse training data, and continuous iteration. At Gladia, we’ve made these priorities central to how we build.
Purpose-built ASR models typically outperform general-purpose LLM-based tools, especially when it comes to real-world speech diversity. Gladia’s ASR can handle a wide range of voices, accents, and speaking styles—not just “standard” or U.S.-centric English.
We take particular care to avoid the common pitfalls that introduce or worsen bias. That includes overfitting to narrow datasets, ignoring underrepresented speech communities, or optimizing purely for speed at the cost of accuracy. Instead, we focus on balanced language coverage and inclusive performance across speaker types.
And whereas some technologies put speed above all else, Gladia strikes the right balance between being fast (low latency) and accurate (very low word error rates).
Noisy speech environments exacerbate ASR bias. The tools struggle to understand certain speech styles in clean, noiseless circumstances, and the disparity explodes in noisy ones. So denoising is a hugely valuable tool.
One study found that selective denoising technology actually reduces biased WER rates between two gender groups.
Our technology includes advanced denoising, which not only improves speech recognition in virtually all scenarios. That includes a meaningful, positive difference between genders, and likely most other speech and language variations.
Automatic speech recognition is transforming business interactions. It already empowers sales and service teams to work faster, more efficiently, and to offer higher-quality customer experiences.
But its full potential will only be realized when it works consistently across languages, accents, and demographics. With the right approach, ASR tools can also become more inclusive, more accurate, and more empowering across languages, accents, and communities.
At Gladia, we believe the future of voice is global, multilingual, and fair. And we’re building the technology to match.
For platforms serious about reaching broader audiences and offering best-in-class voice tools, the choice of ASR partner matters. Partnering with a team committed to reducing bias isn’t just forward-thinking—it’s how you future-proof your platform in a diverse, dynamic world. Get started for free or book a demo to learn more.
Speech-To-Text
As voice agents become a core part of customer and employee experience, users need to know these AI systems are accurate, safe, and acting within boundaries. That’s especially true for enterprise-grade tools, where a rogue voice agent can severely damage relationships and create major legal risks.
Speech-To-Text
Businesses expanding globally face an immediate language barrier. Customers want service in their native tongue, but most companies and call center providers don’t have enough multilingual agents to meet that demand.
Speech-To-Text
Live voice AI is no longer a concept of the future. From customer support to smart IVR (Interactive Voice Response) systems, speech is now transcribed in real time—often before the speaker finishes a sentence.
