02 — Data Type
Audio Annotated Data
Structured audio datasets for speech, voice and audio intelligence.
What it is
A recording is not yet a dataset.
Audio becomes training data when it carries structure: what was said, when it was said, and who said it.
That structure is the difference between a recording and a dataset. Transcription alone is rarely enough — segmentation boundaries, speaker attribution and consistent handling of overlapping speech, disfluency and background events all shape what a model can learn.
Mvaak works to a written annotation guideline so those decisions are made once and applied consistently across a corpus.
Why it matters for AI
Three things that decide whether it trains well.
- 01
Conventions decide the ceiling
How you write numbers, hesitation, overlap and false starts is not a detail. It is a decision the whole corpus inherits, and changing it later means re-transcribing.
- 02
Timing carries as much as text
Word and segment boundaries are what let a model align sound to meaning. Loose timestamps are the most common reason a technically accurate transcript trains poorly.
- 03
Real audio is not clean audio
Background noise, crosstalk and accented speech are the conditions the model will actually meet. Excluding them produces a dataset that flatters the model in testing and fails in use.
Applications
What this data trains.
Each of these draws on the category differently, which is why the specification is written per project rather than per service.
- Speech Recognition
- Voice AI
- Conversational AI
- Speaker Recognition
- Audio Classification
- Multilingual Speech AI
- Audio Intelligence
Collection possibilities
Where the data comes from.
Most engagements are a mix: annotate what you already hold, and collect specifically where coverage is thin.
Working from your recordings
Most audio projects start from material you already hold — support calls, meetings, field recordings — which we transcribe, segment and label to specification.
Prompted collection
Where coverage is missing, audio can be collected against a script or prompt set that targets the accents, phrasings or acoustic conditions the model is weak on.
Language coverage
Language and dialect coverage is confirmed against your requirement at the start rather than assumed, so scope is agreed before recording or transcription begins.
Annotation possibilities
Scoped to the project, not sold as a bundle.
These are the operations available for this data type. Which of them a project uses depends on what the model has to learn.
- 01Transcription
- 02Timestamped segmentation
- 03Speaker labelling and diarisation
- 04Audio event classification
- 05Multilingual handling
- 06Quality and clarity review
Workflow
The same five stages, every project.
Understand
We start from the model and the use case, not the file format — what the system needs to learn, and what would make an example useless.
Specify
Requirements become a written collection or annotation guideline with worked examples and an acceptance definition, agreed before work starts.
Execute
Trained annotators and collectors work to that guideline, with edge cases escalated rather than guessed at.
Validate
Human review against the same rubric, plus consistency checks across annotators before anything is marked complete.
Deliver
Structured output in the schema your pipeline expects, with the guideline and review notes attached.
Quality workflow
Six gates between raw input and delivery.
The process does not change per data type. A corpus assembled across more than one category has to hold together, so the acceptance standard is shared.
Collection
Capture or sourcing against a defined specification, so the input is already in scope.
Guidelines
A written definition of correct, with worked examples and explicit edge-case handling.
Annotation
Trained annotators working to that guideline, escalating ambiguity rather than guessing.
QA
Structural and consistency checks across the batch, not just within single items.
Human Validation
Review by a second person against the acceptance definition.
Final Dataset
Structured delivery with the guideline and review notes attached.
Dataset preparation
What arrives at the end.
The output is agreed before work starts, so delivery is a handover rather than a translation exercise.
- Transcripts with timestamps at the granularity your model consumes — segment, utterance or word
- Speaker labels applied consistently across a session, not reset per file
- Audio events and non-speech markers where the task requires them
- The transcription guideline delivered with the data, including how edge cases were resolved
FAQ
Audio Annotated Data questions.
Transcription, timestamped segmentation, speaker labelling, and classification of audio events or categories, depending on what the model requires.
Other data types
- 01
Egocentric Video Data
Human-perspective video data for systems learning to understand activities, environments and interactions.
- 03
Video Annotated Data
Annotated visual datasets for computer vision and video intelligence.
- 04
Text Annotated Data
Structured language datasets for NLP, LLM and conversational AI.
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