AI English teachers are getting better fast. But until recently, I would have said that most popular AI tools could not really hear your pronunciation. They could understand what you were saying, but they were mostly using speech-to-text first, then responding to the transcript.

That is different from actually listening to the sounds you make. Pronunciation feedback depends on details like vowel sounds, consonants, word stress, rhythm, flow, and clarity. If the AI only sees text, it may miss the exact pronunciation issue.

In this video, I tested ChatGPT, Grok, Claude, and Google Gemini to see whether they could give useful English pronunciation feedback from real audio. Gemini was clearly the most impressive, but the results were still mixed. Then I took the next step and used Google AI Studio to build a simple pronunciation tool with Gemini 3.

Can AI tools actually hear your pronunciation?

That was the first question I wanted to test. If you had asked me before this whether popular AI tools could really hear pronunciation, I would have said no. Not really.

The problem is that many tools respond to speech only after converting it into text. That may be enough for a normal conversation, but it is not enough for pronunciation feedback. If the tool cannot hear the sound difference between ship and sheep, or between we and v, the feedback will be limited.

In the test, ChatGPT could not reliably analyze the audio without creating an exact transcript. Grok could not process the file type I used. Claude started the task, but ran into a limitation. Gemini was the only tool that seemed able to listen to the audio and give specific feedback.

What made Gemini different?

Gemini was different because it seemed to process the audio itself, not just the transcript. When I tested it with three different clips, it correctly identified the broad accent category each time: Indian English, Chinese English, and Latin American Spanish-influenced English.

That alone was interesting. It also described the speaker’s energy and rhythm in ways that made sense. For one clip, it described the delivery as commanding and staccato, with deliberate pauses. That matched what I heard.

But the more important question was whether Gemini could identify specific pronunciation issues accurately.

Was Gemini’s pronunciation feedback accurate?

Partly. Some of the feedback was useful. Some of it was questionable.

In the first clip, Gemini identified a possible w versus v issue in the phrase “we fear technology.” It also noticed some vowel differences, such as the sound in words like fail and way. I could hear what it was talking about there.

But it also missed things I would have focused on first as an English teacher, such as some T and D sound patterns. So the analysis was not bad, but it was not as strong as what a trained human teacher could provide.

In the Jack Ma clip, Gemini correctly identified a Chinese English accent and noticed some issues, such as the pronunciation of model. That matched what I heard. But it also pointed out some things I did not clearly hear in the clip.

In the Sofia Vergara clip, Gemini correctly identified a Latin American Spanish accent and caught one issue I had already noticed: the short i sound sometimes moving closer to a long ee sound, as in is. But again, some of its other points felt like guesses based on common Spanish-speaker patterns rather than careful listening to that specific recording.

What is the main problem with AI pronunciation feedback right now?

The main problem is confidence. Gemini sounded confident even when some of the details were not clearly correct.

This is risky because pronunciation feedback needs to be specific. If the AI says there is a problem with a sound, but that sound was actually fine in the recording, the learner may waste time practicing the wrong thing.

This is why I would not treat Gemini as a perfect pronunciation coach yet. It is useful, but you still need to be skeptical.

What happened when I built a pronunciation tool in Google AI Studio?

The more interesting test came after that. Instead of just dropping audio files into Gemini and asking for feedback, I used Google AI Studio to build a simple pronunciation coach with Gemini 3.

I wanted to see if it could pass a test that AI tools usually fail: the usually test. Can the tool understand a non-native English speaker trying to say the word usually, identify what went wrong, and teach the sound correctly?

This word is difficult because it includes the sound in words like vision, decision, and usually. Many learners struggle with it, and many AI tools completely lose track when you ask them to explain it clearly.

I spent about 30 minutes vibe coding a pronunciation tool in Google AI Studio. I am not a developer, and I do not want to become a professional developer. But this kind of building feels different. It feels like a new form of creativity: you start with an idea, describe what you want, test it, fix it, and slowly turn it into something you can actually use.

Did Gemini 3 pass the usually test?

In my test, I gave the tool a sentence that included usually and unusually. Then I read it badly on purpose, with several pronunciation mistakes.

The tool gave me a low score, which made sense because I had intentionally made many mistakes. More importantly, it caught almost everything I said wrong. It noticed that I mispronounced usually. It caught missing endings, incorrect middle sounds, added extra vowels, and problems with specific words.

It did miss one thing. I said dem instead of them, and it did not seem to catch that. So it was not perfect.

But the most important part was that it identified the real mistakes I had made. When I clicked on the highlighted words, it explained what was wrong and why. That is much closer to the kind of feedback I would give a student myself.

What made the pronunciation tool useful?

The tool was useful because it did not just give a general score. It gave a breakdown. It showed specific problem words, explained the mistake, and gave me a native-speaker version to listen to.

It also generated an audio coaching explanation. The coach explained that I was replacing the zh sound in words like vision, decision, and usually with an R-like sound. It gave a physical tip: round the lips slightly and buzz the vocal cords, like a voiced version of a “sh” sound.

That was exactly the kind of explanation I would want a pronunciation coach to give. It was specific, practical, and connected to the mistake I actually made.

How can you build a simple AI pronunciation tool in Google AI Studio?

If you want to experiment with this yourself, the goal is not just to ask Gemini, “How was my pronunciation?” The better approach is to build a tool with a clear process: generate a speaking challenge, record the learner’s audio, analyze the recording, then give specific feedback.

A useful pronunciation tool does not need to be complicated at first. It just needs the right pieces.

In the version I built, the tool has a few main parts. A fast text model generates short practice challenges. A stronger multimodal model listens to the learner’s audio and compares it against the reference text. A text-to-speech model creates the native version, the word-level playback, and the spoken coaching feedback.

The frontend can be simple: React, TypeScript, Vite, Tailwind CSS, and browser audio tools like MediaRecorder. The important thing is not the exact stack. The important thing is the learning loop: speak, analyze, highlight, explain, listen, repeat.

What should a good AI pronunciation tool include?

If you want to build a pronunciation tool with Gemini or another multimodal AI model, start with the feedback structure. The learner should know what they said, what went wrong, why it matters, and what to practice next.

What should the AI feedback look like?

The feedback should be compact, visual, and practical. In my tool, the useful parts were the score, red highlighted words, clickable explanations, a coach note, native playback, and a problem word list.

This is more useful than a vague comment like “Work on your pronunciation.” The learner can see the exact words, hear the correct version, understand the mistake, and immediately try again.

Should AI pronunciation tools correct accents?

No. The goal should not be to remove someone’s accent. The goal should be clearer communication.

Many English learners speak with an accent and are easy to understand. That is fine. A good pronunciation tool should focus on sounds, rhythm, or stress patterns that cause confusion.

For example, if someone says rice and it sounds like lice, that may cause confusion. If someone has a noticeable accent but the meaning is clear, that may not need correction.

This is why I prefer the phrase clarity feedback instead of accent correction.

What are Gemini’s current limitations?

Gemini is impressive, but I would not fully trust it yet as a pronunciation coach.

So yes, Gemini can be useful. But you should treat it as a practice assistant, not a perfect pronunciation expert.

Why this matters for English learners

This matters because English learners often need feedback that is specific, immediate, and repeatable. A teacher can do this, but a teacher is not always available when you are practicing alone.

A tool like this could help learners practice difficult sounds, hear a native version, compare their own recording, and get a short coaching explanation. That is close to the kind of loop I already use when I give pronunciation feedback to students: listen, notice the mistake, explain it, provide a model, and give the student something specific to practice.

The interactive part still needs work. I would want to add better shadowing features, speed controls, and more back-and-forth practice. But as a simple version built in a short amount of time, it is impressive.

How can English learners use Gemini for pronunciation practice?

Use Gemini to get quick feedback on short recordings. Do not upload a 10-minute speech and expect perfect analysis. Start with 20 to 60 seconds.

This creates a feedback loop. That is where improvement happens.

Is Gemini the best AI English tutor for pronunciation?

For pronunciation feedback specifically, Gemini is one of the most interesting tools right now because it can work with audio directly. In my test, it did more than ChatGPT, Claude, and Grok with the same task.

But the bigger breakthrough may be Gemini 3 as a building tool. In Google AI Studio, it was much easier to create a usable pronunciation coach than I expected. It felt less like coding and more like shaping an idea until it worked.

The real breakthrough is not that Gemini is perfect. It is that AI pronunciation feedback is starting to move beyond transcripts, and AI tools are becoming easier to build for very specific English learning problems.

Final thoughts

Gemini’s pronunciation feedback is not perfect, but it is a meaningful step forward. It seems able to hear more than just the words. It can identify accent patterns, notice some specific sound issues, and give practice suggestions.

The Google AI Studio tool was even more interesting. It caught most of the mistakes I intentionally made, explained the usually sound clearly, gave a native-speaker version, and created a useful coaching explanation.

Still, you should be skeptical. Ask for proof. Ask for exact words. Ask it to focus only on mistakes that reduce clarity. And if a piece of feedback does not sound right to you, test it again.

Used carefully, Gemini can become a useful AI English tutor for pronunciation practice. Not a replacement for a skilled teacher, but a powerful practice tool.

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