Transcription and segmentation
Verbatim or clean transcription to a defined convention, with segment boundaries and timing handled to your specification.
AI Data Annotation (Audio & Multimodal)
Audio and multimodal data, labelled consistently
Transcription, speaker separation, intent and sentiment, and datasets where text, image and audio have to agree with each other.
- Audio and speech
- Multimodal pairing
- Review tier
What this covers
Audio labelling carries ambiguities that visual work does not. Where one speaker stops and another starts, what counts as a disfluency, whether an utterance is sarcastic — all of these need a rule, and different reasonable people will answer them differently without one.
Multimodal work adds a second problem: the label has to be consistent across modes. A caption that describes something not in the image, or a sentiment tag that contradicts the transcript, teaches the model a relationship that does not exist.
StaffCloud handles both with the same discipline as the visual work — written guideline, calibration, review tier, measured agreement — adapted to the ambiguities these data types actually have.
What the team does
Speaker diarisation and tagging
Speaker separation and labelling, including the overlapping-speech and unknown-speaker rules that decide whether the data is usable.
Intent and sentiment labelling
Utterance-level intent, sentiment or category labels against a defined scheme, with the ambiguous-case rules written rather than left to judgement.
Multimodal pairing
Text, image and audio labelled as a set with cross-mode consistency checked, because that consistency is exactly what the model is learning.
Audio event labelling
Non-speech events, environmental sound and acoustic scene labelling to a defined taxonomy.
Review and agreement
A defined review sample with agreement measured, which matters more here than in visual work because the ambiguities are less visible.
How the engagement runs
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01
Convention and guideline
Transcription convention, label scheme and ambiguity rules agreed — the disfluency and overlap decisions in particular, because they are where consistency breaks.
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02
Pilot and calibration
A pilot set labelled and reviewed together, with agreement measured before volume.
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03
Production
Volume work with the agreed review tier and escalation path.
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04
Delivery and reporting
Data delivered in your schema with agreement and defect reporting.
Why audio needs the guideline more, not less
Visual disagreements are visible in a glance. Audio disagreements hide in the transcript until the model learns them.
- Transcription conventions applied the same way throughout
- Speaker and overlap rules decided rather than improvised
- Cross-mode consistency checked rather than assumed
Platforms and tools we work in
The pod works inside your stack. These are the systems this team uses day to day — if yours is not listed, we will learn it.
- Label StudioAudio and multimodal labelling
- AudacityAudio inspection and segmentation
- CVATPaired visual annotation
- PythonValidation and agreement scoring
- Your annotation platformWe work in yours where you have one
Questions buyers ask
The things worth knowing before the first call.
Not sure this is the right scope?
Describe the workload in a sentence and we will point you at the right team — or tell you honestly if it is not something we should take on.
Ask usThat depends on the language and the range of accents in your data, so StaffCloud scopes it against your actual audio rather than publishing a list.
Where we cannot staff a language properly, we say so at that point. Weak labels in a language nobody on the team really hears are worse than no labels, because they look like data and behave like noise.
Whichever your model needs. StaffCloud agrees it with you in the convention step before transcription starts.
Verbatim keeps disfluencies and false starts; clean removes them. Both are defensible and either can be right for your use case. The failure to avoid is a dataset where some files follow one convention and some the other, which is difficult to detect and expensive to unpick.
By a rule agreed with you before the work begins, written into the convention document the whole team works from.
There are several defensible ways to treat overlap and no single correct one. The only genuinely wrong outcome is a different answer file to file, which is what happens when the question is left to individual judgement.
Reliably against a defined scheme with worked examples, yes. StaffCloud builds the scheme with you and calibrates the team against it.
Sentiment has genuinely low natural agreement between people, so the measured agreement figure will be lower than for object detection. We report that honestly rather than presenting the scheme as more objective than it is, so you can judge what the labels will support.
Yes, under terms you set — a named team, scoped access and work inside your own environment where that is required.
Sensitive audio is exactly the case where an anonymous crowd platform is the wrong tool. Knowing who handled a recording, and being able to say so, is often as important as the labels themselves.
Yes. Correcting, re-segmenting and re-labelling existing data is common work and often the fastest route to a usable dataset.
It usually starts with an audit of how consistent the existing labels actually are, because that decides whether the right answer is to repair the set or rebuild part of it. Knowing which before committing budget is worth the audit on its own.