Interview researcher
You have a recording and need searchable words, including names and terminology that matter to the study.
Start with audio-to-text and compare the transcript against a manually checked excerpt.
deepgram transcriptionSpeech workflows
Make sense of deepgram voice for your project
The first decision is whether you need words from audio or audio from words. Deepgram is associated with speech technology, but those are different tasks with different inputs and results. Use this guide to define the job, prepare a small test, and judge the result before planning a larger workflow.
prerequisites
Bring a short, representative sample and a clear definition of success. These starting points help different teams avoid testing the wrong speech task.
Video editor
Dialogue is embedded in a video, and you need text that can support a captioning workflow.
Separate the audio requirement from the editing requirement; a transcript alone is not a finished caption file.
deepgram video transcriptionMultilingual support lead
Callers may speak different languages or switch between them during a conversation.
List the languages in the test set and verify support for the specific task before choosing a workflow.
deepgram languagesVoice interface designer
A spoken response must follow a user's request, not merely appear as text on a screen.
Specify separately how incoming speech becomes text and how a response becomes audible.
deepgram transcriptionone full run-through
Imagine testing a recorded customer question. Keep the trial small enough that a person can inspect every stage.
Define the expected result
Write down whether the goal is a transcript, a spoken answer, or both. For a transcript test, choose a short recording with ordinary speech and one difficult name; keep a human reference of what was said.
Run the appropriate task
Send the recording through the audio-to-text path you are evaluating. If the project also needs a spoken reply, draft the reply as text and evaluate text-to-speech separately rather than treating the transcript as generated speech.
Inspect the handoff
Check the transcript for the difficult name, omissions, and changes in meaning. Then listen to any generated reply for pronunciation and intelligibility. Record which stage needs correction before adding automation.
Related speech paths
The broad Deepgram voice search can point to several narrower jobs. Pick the path that matches the input you have and the output you need.
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1
deepgram transcription
Focus on turning recorded or live speech into text you can review.
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2
deepgram video transcription
Consider the extra extraction and timing work involved when speech comes from video.
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3
deepgram languages
Check language fit against your own speakers and the speech task you intend to run.
options table
This comparison is about task types, not a promise that every control is available through the same interface. Verify current product support before implementation.
Speech to text
Text to speech
1
Starting material
Speech to text
A recording or incoming audio stream
Text to speech
Written text intended to be spoken
2
Primary result
Speech to text
Recognized words for reading or further processing
Text to speech
Audio that a listener can hear
3
Useful test
Speech to text
Compare recognized words with a checked reference
Text to speech
Listen to the output and check clarity and pronunciation
4
Typical mistake
Speech to text
An omitted word or misheard name
Text to speech
An awkward pause or mispronounced name
5
Human review
Speech to text
Read uncertain passages while replaying the source
Text to speech
Listen to a sample in the intended context
6
Next handoff
Speech to text
Search, summarize, caption, or route the text
Text to speech
Play the audio to a listener or place it in an experience
what fails
A deepgram voice workflow still needs checks outside the speech step. These boundaries matter most when people will rely on the result.
- Speech recognition does not verify facts
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A plausible transcript can contain a wrong name, number, or instruction. It records an interpretation of audio; it does not establish that the speaker's claim is true.
WorkaroundReview consequential passages against the original recording and confirm claims from an independent source.
- Generated speech does not write the answer
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Turning supplied text into audio does not decide whether the text is accurate, appropriate, or useful to the listener.
WorkaroundApprove the response text first, then listen to the rendered result before release.
- A short clean clip cannot represent every caller
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Noise, accents, overlapping speakers, and unfamiliar terminology can change the quality of a real-world test.
WorkaroundBuild a small evaluation set from the conditions your audience actually encounters.
- This page cannot confirm current product controls
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Available models, formats, languages, and integration settings can change. A conceptual comparison is not a substitute for checking the current destination.
WorkaroundConfirm the required capabilities and data-handling terms at the service you plan to use.
Take the next step
Test the speech task you actually need
Start with one representative input and one result you can review. The destination may offer a different set of tools than those discussed here, so confirm its current capabilities before committing to a workflow.
- Identify whether audio or text is your starting point
- Keep a human reference for the first test
- Check the result before scaling up
its own FAQ
It can refer to recognizing speech, producing spoken audio, or combining both in an application. Name the input and desired output first so you can evaluate the relevant task rather than an umbrella term.
No. Speech-to-text returns recognized words from audio, while text-to-speech turns written words into audible output. A conversational experience may use both, but each stage needs its own quality check.
Choose a short sample that resembles your actual audience and keep a reference you can inspect. Check names, meaning, and omissions for transcripts; listen for clarity and pronunciation when testing spoken output.
A successful sample is useful evidence for that sample, not proof that every language, accent, or mixed-language conversation will perform the same way. Check current support for the specific task and test recordings from your intended speakers.
No. Captions may also require timing, segmentation, speaker decisions, and editorial review. Treat recognized text as an input to that workflow, then check the captions against the video.