Have you ever tried asking an artificial intelligence (AI) voice assistant a simple question but only for it to misinterpret your words entirely even after you tried asking the same thing multiple times?
Maybe you pronounced something differently, or used a local phrase; maybe you have an accent it didn’t recognize. It’s frustrating, no doubt.
Voice assistants are supposed to make life easier, but when they don’t get what you’re saying, they can feel more like an obstacle than an asset.
Voice assistants are used everywhere, in the smart speakers in our homes to virtual assistants on our phones and in the sat-nav systems of our cars. But while these systems have progressed significantly, they still struggle with one key issue: Dialects.
Voice assistants face a significant challenge in understanding many accents and dialects spoken worldwide. The gap in understanding leads to moments of frustration, abandonment of the technology and missed opportunities for users who don’t talk in the assistants’ preferred way.
Why Do Voice Assistants Struggle with Dialects?
Voice assistants use speech recognition models trained in vast amounts of spoken data. But here’s the problem, most of that data comes from people speaking in a standardized way. That leaves out much real-world variety in how people talk. Here’s why that happens:
- AI Training Data Is Biased
AI models are only as good as the data they learn from. If the bulk of training data comes from one particular accent or way of speaking, the assistant will work better for those users and struggle with everyone else. Some languages have a “standard” version that gets all the attention, while regional dialects get overlooked.
- Different Pronunciations Throw AI Off
Words sound different depending on where you’re from. Think about the word “water”; It’s said differently in American, British and Australian English. And even within those countries, you’ll hear variations based on regions. AI isn’t great at handling these nuances unless it’s specifically trained to recognize them.
- Dialects Have Their Own Words and Grammar
It’s not just about pronunciation, dialects often have different vocabulary and syntaxes. A person from the southern U.S. might say “y’all,” while someone in the U.K. might say “you lot”, same meaning, but if the AI isn’t trained on both, it wouldn’t understand.
- AI Models Aren’t Always Localized
Some voice assistants display superior accuracy for certain languages, but they struggle to recognize dialects. Creating AI models that work for every dialect is expensive and complicated. As a result, companies often deprioritize less-spoken languages for widely spoken ones.
What Happens When AI Doesn’t Understand Dialects?
When AI assistants do not recognize dialects, a class of users gets completely left out from the opportunity of availing its services. Frustration often leads to lack of trust, and if AI gets things wrong too often, people stop relying on it altogether.
It can also cause workplace issues. In industries where voice-activated tech is used widely, employees with strong regional accents find the tools difficult to use and unreliable, causing work to slow down.
How AI is Getting Better at Understanding Dialects
Even though these challenges exist, AI is improving every day. Researchers and companies are working on ways to make voice assistants more inclusive. Here’s how:
- Training AI on More Dialect Data
Instead of just using a single “standard” version of a language, AI teams are now focusing on collecting speech from a broader range of speakers. The more variety there is in training data, the better the AI gets.
- Teaching AI to Learn from One Dialect to Another
With transfer learning technique, AI can be made to apply what it knows from one dialect to another. This means a model trained in standard English can be adjusted to recognize regional variations of the language without starting from scratch.
- Learning from Small Amounts of Data
Some dialects don’t have a lot of recorded speech available. New AI techniques, like self-supervised learning, help models learn from smaller datasets, making it easier to add dialect support.
- AI That Considers Context
Future AI assistants won’t just listen to words, they’ll consider who’s speaking, what they’ve said before and the overall context to interpret different dialects better.
- Using AI to Create More Dialect Data
One remarkable development is synthetic speech, AI-generated voices that mimic different dialects. This helps create training data in cases where real-world recordings are scarce.
What’s Next for AI and Dialects?
As AI keeps evolving, we can expect voice assistants to be able to better handle lingual nuances. Here’s what’s coming:
- Personalized AI Assistants: Future AI could adapt to how you specifically speak over time. Imagine an assistant who learns your unique way of pronouncing words and improves as you interact more with it.
- Understanding Multiple Languages at Once: AI is getting better at code-switching, where someone naturally switches between languages mid-sentence. Future assistants can handle this transition smoothly, making them so much more helpful in multilingual households.
- Regional Customization for Voice Assistants: AI assistants of the future might allow users to choose their dialect preferences or even fine-tune how their assistant understands and responds to them. Think of it like training a pet,. Your AI could become more familiar with your verbal cues and speech patterns over time.
- More Natural Conversations: Instead of relying on fixed scripts, AI assistants are being trained to engage in more natural conversations that adapt to a speaker’s unique verbal styles, including rhythm, pauses and expressions. This will make interacting with AI feel much more natural and human-like.
Voice assistants have come a long way, but they still have miles to go. If AI is to genuinely going to work for everyone, it needs to move beyond standard accents and recognize the full diversity of how people talk. The good news? It is improving daily, and soon voice assistants will better understand the kinds of speech that are beyond its skill today.
