Speaker diarization answers "who spoke when?" in a recording. Upload a conversation, meeting or panel and the AI counts the distinct voices, labels them Speaker 1, Speaker 2 and onward, and lays out a timeline of their turns with start and end times. It marks the exact change points between speakers, notes stretches where people talk over each other, and gives a confidence score for each speaker it separates. Each voice also gets a short profile: estimated gender and age range, plus any accent or language differences that set it apart. The result is a structure you can use to attribute quotes, measure who dominated a discussion, or prepare a recording for transcription. It labels voices by sound; it cannot know anyone's name, and it does not split the audio into separate files.
Example result
Input
A 4-minute recording of a three-person product meeting on a conference room speakerphone.
Output
Distinct speakers: 3. Timeline: 00:00 to 00:48 Speaker 1 00:48 to 01:30 Speaker 2 01:30 to 01:36 Speaker 1 and Speaker 3 overlap 01:36 to 02:55 Speaker 3 02:55 to 04:02 Speaker 1 Speaker 1: adult male, likely 40s, lower pitch, measured pace; speaks for about half the recording and opens and closes the meeting. Speaker 2: adult female, likely 20s to 30s, faster delivery, light British accent. Speaker 3: adult male, likely 30s, farther from the microphone, so quieter and more reverberant. Confidence: Speakers 1 and 2 are clearly separated. Speaker 3 is lower confidence because of distance; two short interjections may belong to Speaker 1.
Scores
How it works
How Speaker Diarization works
- 01
Upload the conversation
Sign in, open the tool and add a recording with two or more people, or record one in the browser. Choose the AI model you prefer.
- 02
Count and cluster voices
The AI groups stretches of speech by voice characteristics such as pitch, tone and accent, and decides how many distinct speakers are present.
- 03
Mark turns and overlaps
It finds the moments where one speaker hands off to another, builds a time-segmented timeline, and flags segments where two voices overlap.
- 04
Profile each speaker
Each label gets a description, a confidence score and a summary of speaking activity, so you can see how the talking time was shared.
Use cases
Who it is for
Preparing interviews for transcription
Get the speaker turns first, then transcribe with confidence about which quote belongs to whom, especially in group interviews.
Meeting and panel reviews
See how talking time was shared, who interrupted, and where people talked over each other, which is hard to judge from memory.
Podcast editing
Use the change points as an edit map when cutting a multi-host episode, or to find a guest's answers quickly.
Call and research recordings
Separate interviewer and participant turns in focus groups or customer calls before coding or analyzing what each side said.
Tips
Get better results
- Place the recorder centrally so every voice reaches the microphone at a similar level; the quietest person is usually the hardest to separate.
- If you know how many people took part, compare that with the speaker count in the report as a quick sanity check.
- Voices that sound alike, such as two people of similar age and accent, are more likely to be merged. Treat their labels with care.
- Keep recordings to a manageable length and split long sessions, since each report has a size limit.
- Cut music beds and intros before uploading so they are not mistaken for extra speakers.
Limitations
What it can't tell you
Diarization here is an AI estimate from the sound of each voice. It does not identify people by name, and speaker labels are not proof of identity. Timestamps are approximate, and similar voices, heavy overlap, distant microphones and short interjections can cause turns to be merged or split incorrectly. Gender and age descriptions are guesses from vocal characteristics and may be wrong. The tool returns a written timeline, not separated audio tracks for each speaker.
Frequently Asked Questions
Speaker Diarization questions
What is speaker diarization?
Speaker diarization is the process of dividing a recording into segments by speaker, so you know who spoke when. It does not require knowing who the people are. It just groups speech by voice, giving each one a label like Speaker 1, and marks the time boundaries of each person's turns.
Is speaker diarization the same as speaker identification?
Not quite. Identification matches a voice to a known person, usually against enrolled samples. Diarization only separates and labels the voices in a single recording. You can then rename the labels yourself. To check whether two separate clips contain the same voice, the voice compare tool is designed for that.
Can it separate speakers in audio into separate tracks?
No. "Speaker separation" sometimes means pulling each voice out into its own audio file. This tool produces a written timeline showing who spoke when, including overlaps, but it does not edit or export audio. Use the timeline to guide cuts in your audio editor.
How many speakers can it handle?
It reports however many distinct voices it hears, from two upward. Accuracy drops as the group grows, especially when voices are similar or people talk at once. Small groups of two to four people in a clear recording give the most dependable timeline.
Does it transcribe what each person said?
This tool focuses on the speaker structure: counts, turns, overlaps and profiles. If you need the words as well, the audio to text tool produces a transcript with speaker labels and timestamps together.
What do I need to run it?
An account with a paid plan. Each recording you analyze uses credits from your monthly balance, and the pricing page lists what each plan includes.
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Speaker Diarization uses credits from a paid LindaleAI plan. See pricing