Audio Speaker Turn Detector

Audio Speaker Turn Detector | Online Free Tool | Elysia Tools

Tool usage guide

Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Category: Media
Input types: file, select, number
Output type: file
Sample coverage: 4
API ready: Yes

Overview

The Audio Speaker Turn Detector analyzes speech-range audio files to identify likely acoustic turn changes by comparing adjacent windows for spectral balance, zero-crossing rate, and volume level. It generates an approximate timeline of speaker transitions, allowing you to export the markers in JSON, CSV, or SRT formats to streamline audio editing and transcription workflows.

When to use

When you need to segment a raw interview or podcast recording into distinct conversational turns before editing.
When preparing audio files for transcription and you want to pre-mark timestamps where the speaker changes.
When analyzing dialogue recordings to map out the pacing and frequency of speaker transitions.

How it works

1Upload your dialogue or interview audio file in a supported audio format.
2Adjust the detection sensitivity and set the minimum duration in seconds for a valid speaker turn.
3Choose your preferred export format, such as JSON, CSV, or SRT.
4Run the analysis to process the audio and download the generated timeline of acoustic turn changes.

Use cases

Segmenting podcast episodes to quickly locate and edit transitions between the host and guests.
Generating SRT subtitle templates with pre-placed time blocks for manual transcription.
Analyzing raw research interviews to map out conversational turn-taking dynamics.

Examples

1. Podcast Editing Preparation

Podcast Editor

Background

A podcast editor has a 45-minute raw interview recording with two speakers and needs to cut out cross-talk and silence.

Problem

Manually finding every speaker transition in a long waveform is tedious and time-consuming.

How to use

Upload the WAV audio file, set the sensitivity to 'balanced', set the minimum turn to 1.0 second, and select 'SRT turn markers' as the export format.

audioFile: interview.wav, sensitivity: balanced, minimumTurnSeconds: 1.0, exportFormat: srt

Outcome

An SRT file containing timestamped blocks indicating every time the acoustic profile changed, which can be imported directly into video or audio editing software.

2. Research Interview Segmentation

Academic Researcher

Background

A researcher is analyzing a recorded focus group discussion to study turn-taking patterns.

Problem

The researcher needs a structured dataset of when speakers likely switched to calculate average turn lengths.

How to use

Upload the MP3 recording, set the sensitivity to 'sensitive' to capture quick exchanges, set the minimum turn to 0.5 seconds, and choose 'CSV timeline'.

FAQ

Does this tool identify who is speaking?

No, it only detects acoustic changes between adjacent speech windows and does not perform speaker identification or diarization.

What file formats can I export the timeline in?

You can export the speaker turn timeline as a JSON file, a CSV table, or SRT subtitle markers.

How does the sensitivity setting affect the results?

The 'Sensitive' option detects minor acoustic shifts but may cause false positives, while 'Conservative' only flags major changes, reducing false triggers.

Can I set a minimum duration for each speaker turn?

Yes, you can configure the minimum turn duration between 0.2 and 30 seconds to filter out short interruptions or noise.

Is this tool using AI to transcribe the audio?

No, it uses deterministic acoustic analysis based on spectral balance, zero-crossing rate, and signal level, without transcribing text.

audioFile: focus_group.mp3, sensitivity: sensitive, minimumTurnSeconds: 0.5, exportFormat: csv

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Learn when to use this tool, what it supports, and how real users apply it.

Key facts

Overview

When to use

How it works

Use cases

Examples

1. Podcast Editing Preparation

2. Research Interview Segmentation

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