How AI Transcription Actually Works

Five years ago, automated transcription was a joke. You'd get back garbled text that took longer to fix than it would have taken to type everything yourself.

That's not the case anymore. Modern AI transcription is genuinely good. We're talking 95%+ accuracy for clear audio, with support for multiple speakers, accents, and even noisy environments.

So what changed?

The Short Version

Modern speech recognition uses deep learning models trained on hundreds of thousands of hours of audio. These models don't just match sounds to words. They understand context. If someone says "I need to check the books," the model knows from context whether they mean accounting ledgers or something to read.

The models are also trained on real conversations, not just people carefully reading scripts. So they handle the ums, the interruptions, and the crosstalk that come with actual human speech.

Speaker Diarization: Who Said What

One of the most useful features in modern transcription is speaker diarization. This is the ability to tell speakers apart and label who said what.

It works by analyzing the audio characteristics of each voice, things like pitch, speaking pace, and vocal tone. The AI clusters these into distinct speakers and labels the transcript accordingly.

This is huge for meetings. Instead of a wall of text, you get a conversation you can actually follow.

What Makes a Good Transcription Tool

Not all transcription services are equal. Here's what we think matters:

Accuracy. This is table stakes. Anything below 90% creates more work than it saves.

Speed. You shouldn't have to wait 20 minutes for a 5 minute recording. Good tools process audio in near real time.

Speaker labels. If the tool can't tell speakers apart, meeting transcripts are basically useless.

Punctuation and formatting. Raw text without any structure is hard to read. Look for tools that add proper punctuation, paragraphs, and formatting automatically.

How Lua Voice Handles It

We use AssemblyAI for transcription, which consistently ranks among the most accurate engines available. Every transcription includes automatic speaker diarization and proper formatting.

But we also took it a step further. After transcription, we use Claude to generate structured notes with summaries, key points, and action items so you don't have to read through the entire transcript to find what matters.

The result is a workflow that takes you from raw audio to organized, actionable notes in minutes. No manual cleanup required.