Speech AI
Transcription conventions decide your model's ceiling
How you write numbers, hesitation and overlap is not a detail. It is a decision the entire corpus inherits, and changing it later means starting again.
3 min read
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.
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.
Where the data comes from
Annotate what you hold. Collect 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 operations
Scoped to the project, not sold as a bundle.
- 01Transcription
- 02Timestamped segmentation
- 03Speaker labelling and diarisation
- 04Audio event classification
- 05Multilingual handling
- 06Quality and clarity review
What arrives at the end
The output is agreed before work starts.
- 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
What this data trains
- Speech Recognition
- Voice AI
- Conversational AI
- Speaker Recognition
- Audio Classification
- Multilingual Speech AI
- Audio Intelligence
Quality workflow
Six gates between raw input and delivery.
The process does not change per data type, so a corpus assembled across categories holds together.
- 01
Collection
Capture or sourcing against a defined specification, so the input is already in scope.
- 02
Guidelines
A written definition of correct, with worked examples and explicit edge-case handling.
- 03
Annotation
Trained annotators working to that guideline, escalating ambiguity rather than guessing.
- 04
QA
Structural and consistency checks across the batch, not just within single items.
- 05
Human Validation
Review by a second person against the acceptance definition.
- 06
Final Dataset
Structured delivery with the guideline and review notes attached.
FAQ
Audio Annotated Data questions.
Transcription, timestamped segmentation, speaker labelling, and classification of audio events or categories, depending on what the model requires.
Yes. Language coverage is confirmed against your requirement at the start of a project rather than assumed.
Through a written guideline covering conventions and edge cases, followed by human review of completed work against that same guideline.
In a structured format agreed with your team, with the annotation guideline supplied alongside the data.
Yes, and often that is the point — models deployed into real conditions need training data that reflects them. What matters is agreeing upfront how unintelligible passages are marked rather than silently dropped.
Related reading
All articlesDataset Quality
Inter-annotator agreement, and what it actually tells you
A single agreement score is a weak summary of a rich signal. What matters is where the disagreement sits and whether it is structured.
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Data Annotation
Annotation guidelines are the real deliverable
Two annotators disagreeing about the same example is not a training problem. It is a definitions problem, and it is far cheaper to solve before collection than after.
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