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.
Speech to text for conversations, not just dictation
Many speech-to-text tools are designed for one person speaking into a microphone. Media2Text is built for recorded speech that already exists: meetings, interviews, classes, sermons, focus groups, depositions and podcasts. Instead of typing as you talk, you upload a finished recording and receive a structured transcript you can review against the audio.
Speaker labels and renaming
When automatic speaker detection is on, each segment is attributed to a speaker. You can rename speakers, hide speakers you do not need and export documents with or without names. Speaker attribution works best when people take turns; heavy crosstalk or several people on one distant microphone will need more manual correction.
Timestamps you can trust during review
Every segment carries a timestamp, and clicking the text moves playback to that moment. Word-level timing is used when the transcription engine returns reliable word timestamps; otherwise segment timing is used. This makes it practical to verify a quote, check a number or confirm who said what without listening to the whole recording again.
Where recorded speech goes next
A meeting transcript can become minutes, a lecture transcript can become study notes, and an interview transcript can become evidence in a research report. Export Markdown or DOCX for writing, CSV or JSON for analysis, or SRT and VTT if the recording will be published with captions. For audio-only files see audio to text; for recordings with picture see video to text. See the guides for meeting recordings, lectures and podcasts.
With an account, the workspace can also produce AI summaries in General, Meeting, Course, Interview and Podcast styles, and mind maps that link each point back to the supporting transcript. These outputs help you navigate a long recording, but important names, figures and quotations should still be checked in the transcript itself.
Languages, accents and recording quality
Media2Text documents 60 transcription languages with automatic language identification, including English, Spanish, Portuguese, French, German, Japanese, Korean, Chinese and Tagalog. Accuracy depends more on recording conditions than on accent alone: a close microphone, a quiet room and one person speaking at a time make the biggest difference. If you are recording a future session, a simple lapel or desk microphone near the main speaker usually improves results more than any setting change after the fact.
Free trial and limits
You can try the service with up to 2 lifetime transcription tasks sharing 75 minutes in total, without adding a card. Each file can be up to 6 GB, and paid plans add unlimited uploads with files up to 5 hours 15 minutes long.
Guides for specific recordings
Step-by-step guides cover transcribing interviews for research and journalism, and turning meeting recordings into minutes.
Privacy for recorded conversations
Conversations often contain personal or confidential information. Ask participants for consent before recording, and remove recordings you no longer need. For video files, supported browsers extract the audio locally so the original video stays on your device; prepared audio is uploaded securely and retained according to the published privacy policy. The security page explains the data flow in more detail.