Key Takeaways
A transcript can get every word right and still put it under the wrong name, because recognising speech and assigning it to a speaker are separate operations. Speaker labelling works from voice characteristics, so it does best with a few clearly different voices picked up at similar volume by one microphone. It struggles with people talking over each other, with quiet or late-joining speakers, and it will never hand you real names on its own — that part you add afterwards.
If you have ever opened a transcript of a group conversation and found half of it attributed to the wrong person, the words themselves were probably fine. The problem sits one layer up, in the part of the process that decides who was talking, not what they said. That part runs on different information and fails in different ways, and almost all of what determines whether it works is decided at recording time, before any software touches the file.
This is the part worth understanding if you are about to record an interview, a panel, or a family conversation and you actually need to know who said what — not just what was said. Some of it is about where you put the microphone. Some of it is about accepting that certain situations will never label cleanly, no matter what you run them through.
Two separate jobs, one transcript
Recognising words and deciding who spoke them are not the same task, even though they end up in the same document. The first job listens to sound and works out language — it is largely indifferent to whose voice is making it. The second job listens to the same sound and asks a different question: does this voice match the one from two sentences ago, or is it a new speaker? That second question is answered from voice characteristics — pitch, tone, cadence, the acoustic fingerprint of a person’s speech — not from the words themselves.
This is why a transcript can be word-for-word accurate and still misattribute lines. The transcription succeeded. The speaker separation did not. They can fail independently, and when someone tells you a transcript is ‘wrong’, it is worth checking which job actually failed, because the fix is different depending on the answer.
What makes speaker separation work
Speaker separation is at its best with a small number of voices that sound clearly different from one another, all picked up by one microphone position that hears everyone at roughly the same volume. That is the ideal case: two or three people, distinct voices, one recording device sitting somewhere it can hear the whole room evenly.
Every deviation from that ideal costs you something. More speakers means more chances for two voices to sound similar enough to blur together. Similar-sounding voices — two people of similar age and accent, for instance — are harder to tell apart than a deep voice and a high one. And if one person is picked up much louder than another, the system starts treating volume differences as if they were speaker differences, which they are not.
None of this is exotic. It is closer to what a person would notice too, if they closed their eyes and just listened. The software’s advantage is patience and consistency across an hour of audio, not some extra sense a listener lacks.
Where it falls apart: people talking over each other
There is one situation that reliably breaks speaker labelling, and no amount of good setup fixes it: genuine crosstalk, where two people are speaking at the same moment. During overlap there is no clean signal belonging to one voice — the audio is a mix of both, and there is nothing to cleanly attribute. What usually happens is that one speaker’s words get folded into the other’s turn, so the transcript shows a single continuous statement that was actually two people talking simultaneously.
This is not a bug to be patched later. It is a limit built into the nature of the problem — you cannot unmix two overlapping voices into two clean streams after the fact, not reliably. The honest expectation is that any part of a recording with real overlap will need a human pass to sort out who said what, or will simply stay attributed to whoever the system guessed. If your conversation has a lot of people talking over each other — a heated panel, an argumentative family dinner — plan for that part of the transcript to need manual correction, and do not judge the whole tool by that section.
Where you put the microphone matters more than what it is
Distance to the microphone affects speaker labelling more than the quality of the microphone does. A phone sitting in the middle of a table will usually give you better speaker separation than a nicer microphone that happens to be sitting next to one person, because the quiet, farther-away participants are exactly what a system loses first. If someone is consistently much quieter than everyone else because they are the farthest from the mic, that gap in volume can matter more to speaker labelling than any difference in equipment.
For a group conversation, this points to a simple rule: put the recording device somewhere central, not somewhere convenient. The middle of the table, not the end nearest the host. If people are spread around a room rather than seated close together, that is a harder case regardless of equipment, and it is worth knowing that going in rather than discovering it afterward.
Getting names instead of Speaker 1, Speaker 2
Nothing in the audio itself tells a transcription system anyone’s name — it can tell that two different voices are present, but it has no way to know one of them is called Priya and the other is called Tom. So the output comes out labelled with anonymous identifiers: Speaker 1, Speaker 2, and so on. Turning those into real names is a manual step, and it only needs doing once, at the top of the transcript, matching each label to a name.
The one thing that makes that step easy rather than tedious is having everyone say their name out loud near the start of the recording. It costs about ten seconds and gives you a clear anchor — you know that the voice saying ‘this is Tom’ near the beginning is the one to label Tom for the rest of the document. Skip that and you are left matching voices to names from memory or context, which works but takes longer and is more error-prone the more speakers there are.
If you are recording an interview specifically, this habit is worth building into how you start every session — see the notes on transcribing interviews for more on setting one up well from the first minute. For turning a finished recording into labelled text once you have it, converting audio to text covers the actual conversion step, and an app such as the transcription app from SozAI can produce speaker labels, summaries and translation across a wide range of languages once you have the recording — naming the speakers afterward is still on you, but it is the fast part.
What to expect realistically
Even with a good setup, a few situations produce predictably imperfect results, and it is worth knowing them ahead of time rather than treating them as failures.
- A speaker who joins partway through a recording, after the system has already settled on its voice profiles, is often merged into an existing speaker rather than given their own new label.
- Someone who only says a handful of words across the whole recording — a quick agreement, a one-line question — frequently gets folded into whoever spoke around them, because there is not enough of their voice for the system to build a separate profile from.
- Genuine overlapping speech, as covered above, tends to collapse into one speaker’s turn.
- A large group with many similar-sounding voices will produce more mislabelling than a small group with distinct ones, simply because there are more chances for confusion.
None of these are reasons to skip transcribing a difficult recording — they are reasons to expect a short manual cleanup pass on the sections where they occur, rather than assuming the whole document needs rechecking. If you are recording something you know will have this kind of complexity, it is also worth deciding in advance whether you need every word transcribed or just the labelled highlights; transcribing everything is not always the useful choice, particularly for long recordings where you mainly need to know who committed to what — the kind of use case covered under turning recordings into meeting notes. And if you want to test how a given recording labels before committing to a longer file, grabbing the app from the download page and running a short sample first will tell you more than any general advice can.
What actually moves the needle
If you take one thing from all this, it is that the recording matters more than the software. A central microphone, a small number of clearly different voices, everyone saying their name at the start, and an acceptance that overlapping speech will need a manual look — that combination will get you further than any setting or feature choice made after the fact. The labelling step is doing real work, but it is working with whatever the recording gave it, and a recording made with speaker separation in mind will always label better than one made without it, regardless of what runs the transcription afterward.
