3 Common Problems You'll Face in Your Voice AI Project

This blog post is based on what we learned after building and launching conversational AI in just 43 hours.

You might be working on a voice AI project, like a customer service bot, an interview tool, or a voice assistant for your product.You set up speech-to-text, connect a language model, and add text-to-speech. It works well in controlled tests.

But when you try it with real users, things start to fall apart:

• The conversation feels robotic
• The system interrupts people mid-sentence
• It responds to things nobody said
• Users get frustrated and quit

Let’s look at what usually goes wrong and how you can fix it.

Problem 1: Your system doesn't know when someone finished talking

Your system needs to know when someone has finished speaking so it can respond. If you get this wrong, the conversation won’t work smoothly.

At first, you might think measuring volume is enough. If the audio is loud, someone is talking. When it gets quiet, they’re done.

But this approach quickly fails when you test with real users.

Loud keyboard clicks can be mistaken for speech, so your system might interrupt to respond to typing. Quiet speakers may get cut off because their voices don’t reach the set threshold. Background noise from fans, traffic, or people can also cause problems.

This happens because volume only measures how loud a sound is, not whether it’s actually speech.

What works: Use a voice activity detection model that looks at speech patterns instead of just volume.

We use Silero VAD. It runs in the browser and returns a probability score for each audio frame. Speech scores 0.7-0.9. Noise scores 0.1-0.3. Keyboard clicks score 0.1-0.2.

This approach fixes most false positives right away.

However, detection by itself isn’t enough. You also need to decide when your system should respond.

If your system responds as soon as someone stops talking, it might interrupt them while they’re still thinking. People often pause to find the right words, breathe, or think.

But if you wait too long, the conversation feels slow. Even two seconds of silence after each answer can seem awkward.A better solution is to use two-phase processing.

• Begin transcribing after 250 milliseconds of silence, running this in the background. If the person starts talking again, just cancel the transcription.
• Wait 600 milliseconds before confirming they’ve finished and then send the input to your AI.
• Adjust your timing based on how long the answer is. Short answers should get quicker responses, while longer answers need more patience.

This approach helps the conversation flow in a more natural way.The main point: Don’t rely on volume. Use voice activity detection with two-phase timing instead.

Problem 2: Your speech recognition invents things people never said

OpenAI’s Whisper is one of the best speech-to-text tools out there, but it sometimes makes up things that weren’t said.

If you give it silence or background noise, it can invent random phrases like:

• "Thanks for watching!"
• "Subscribe to my channel!"
• "[Music]"

These are just random phrases it picked up from its training data.

This isn’t a rare problem. It often happens with real users, especially during pauses, with background noise, or when there’s an echo from your own audio output.

If you don't prevent this, your AI responds to things users never said, and the conversation becomes nonsense.

What works: Layer multiple defenses. Each catches what the others miss.

1. Voice detection first

Transcribe only the audio that your voice activity detection marks as real speech. If the probability is below 0.5, skip transcription altogether.

2. Echo prevention

• Keep track of when your system is speaking, and don’t process any audio during those times.
• Set a flag to block your speech pipeline while your AI is talking.
• Add another flag to prevent processing right after your AI finishes.

This helps avoid transcribing echoes.

3. Duration filters

Make sure there’s at least 500 milliseconds of detected speech and at least 1000 bytes of audio data before processing. Short bursts of noise won’t meet these limits.

4. Clean audio first

Use noise reduction on your backend before sending audio to speech recognition. This removes background noise and gives your model cleaner input.

We use spectral gating that removes 75% of detected noise.

The main takeaway: Build four layers of defense. Doing this reduced our false transcriptions by 99%.

Problem 3: Every millisecond of delay kills conversation flow

Natural conversation moves fast. When someone asks a question, you respond within a second or two. Longer feels awkward.Voice AI involves multiple steps:

• Detect speech ended (100-250ms)
• Transcribe audio (200-800ms)
• Send to language model (300-900ms)
• Generate voice response (200-400ms)
• Stream to avatar or speaker (100-200ms)

That's a minimum of 1.5-2.5 seconds.

Your instinct will be to optimize each step individually. Better transcription model, faster language model, quicker voice generation.

This can help, but you’ll quickly reach a point where further improvements don’t make much difference.

What works better: Overlap steps aggressively and start processing speculatively.

• Run voice detection continuously
• The moment the speech ends, start transcription on your backend
• While transcription runs, prepare your conversation context
• The moment you have text, send it to your language model
• Stream the response to voice generation
• Start playing audio before generation completes.

Use your two-phase processing to your advantage. By starting transcription speculatively at 250ms, you save 350-600ms in cases where the person was actually done speaking.

With this method, we reached a total latency of 1.1 seconds. If it takes more than 2 seconds, every exchange feels like a bad phone call. At around 1 second, the conversation feels mostly natural.

The main point: Don’t just focus on speeding up each step. Overlap them and start processing early whenever you can.

What This Means for Your Own Project

Most of your time will go into three main areas.

1. Voice activity detection

Expect to spend time here. The obvious approach (measure volume) doesn't work. You need to implement proper detection and tune timing for your use case.

Budget: 4-8 hours

2. Hallucination prevention

You might not realize you need this until you test with real users. Then you’ll have to research solutions and build several layers of defense.

Budget: 4-8 hours

3. Latency optimization

This part is ongoing. You’ll need to try different methods, measure real latency, and find the best balance between speed and accuracy for your project.

Budget: 4-8 hours

4. Other steps

Everything else, such as connecting APIs, building basic conversation flows, or handling audio streams, takes less time than these three problems.

The Timeline Reality

We built our AI recruiter in 43 hours total. Here's where time actually went:

• 12 hours: Basic setup and connecting the pieces. This part moves fast because you're wiring together existing tools.
• 8 hours: Voice activity detection. Four hours on an approach that failed (volume-based), four hours on one that worked (Silero VAD with two-phase timing).
• 8 hours: Developing an assessment system tailored to our specific use case. This is domain work that takes time regardless of tools.
• 4 hours: Face verification for identity checking. Finding the right models, understanding anti-spoofing, and making it work locally.
• 4 hours: Hallucination prevention after discovering the problem in testing. Research plus implementation of a four-layer defense.
• 4 hours: Latency optimization. Testing different approaches and building speculative processing.
• 3 hours: Everything else, like admin interface, polish, and documentation.

The pattern is clear: Connecting existing tools is quick, but solving the three main conversation problems takes real effort.

How to Approach Your Build

Begin with the basics

Wire up speech-to-text, language model, and text-to-speech. Get basic conversation working in controlled conditions. This gives you something to test with.

Budget: 4-12 hours, depending on your stack.

Test with real users immediately

Don’t wait until everything is perfect. Test in real environments with background noise, poor microphones, and unpredictable user behavior.

You’ll quickly find the three main problems this way.

Fix voice detection next

This affects everything else. You can't test conversation quality or optimize latency until you can reliably detect when someone stops speaking.

Set up proper voice activity detection with two-phase timing. Test it with quiet speakers, background noise, and interruptions.

Build hallucination prevention

Add the four layers. Test each one individually to understand what it catches. You'll find that each layer catches cases that the others miss.

Optimize latency last

Once detection and accuracy are working, focus on speed. Overlap processing steps, start transcription early, and measure real latency with actual users.

The Lessons

• The obvious approach doesn't work
• One layer of defense isn't enough
• Speed comes from architecture, not just faster models
• Testing in controlled conditions hides problems
• Some problems just take time

Developing quickly doesn’t mean skipping the tough problems. It means knowing which ones are hard, so you don’t waste time on issues that are already solved.

What to Do Next

If you're building voice AI:

• Budget time for the three core problems
• Test with real users early
• Build defenses in layers
• Accept that some approaches will fail
• Focus on conversation quality over feature count

The key difference between voice AI that works and voice AI that frustrates users is how well you solve these three problems.​