Speaker context
Automatic speaker labels separate voices so a conversation is easier to scan. Rename speakers before sharing or exporting.
RECORDED SPEECH RECOGNITION
Make spoken conversations searchable.
Convert speech from meetings, interviews, lessons and voice recordings into text with timing and speaker context. Review the result in a focused workspace, then export it for the next step.
Turn speech into text →Structured for listening and reading
Playback-linked text
Use timing information to move between a transcript and its source audio instead of searching by ear.
Review tools
Search, edit and copy the transcript in one workspace. AI-generated text should always receive a final human check.
A practical speech-to-text workflow
- Start with the clearest recording available. A close microphone and low background noise usually reduce recognition errors.
- Select or detect the language. A correct language hint can help with short clips or multilingual environments.
- Verify the important parts. Compare names, numbers, quotations and overlapping dialogue with playback before export.
Where a speech transcript helps
Interviewers can retrieve answers without scrubbing through an hour of audio. Teams can turn recorded discussions into searchable source material. Educators and students can revisit a particular explanation by time. Creators can develop captions, show notes and written excerpts from the same recording.
Speaker detection groups similar voices; it does not identify a real person's name. Results can change when people interrupt each other, speak briefly or use similar voices. Rename and verify labels before distributing a transcript.
Speech recognition questions
Does speech to text work live?
This page focuses on uploaded recordings rather than live dictation. Record your session, then upload the saved audio or video.
Can it tell speakers apart?
Media2Text can create automatic speaker labels. You can review and rename those generic labels in the transcript workspace.
Is the result always exact?
No speech-recognition model is perfect. Noise, accents, specialist vocabulary and overlapping voices can affect accuracy.