Audio annotation.
Transcription, speaker identification, and classification across languages, accents, and dialects — reviewed by trained annotators, not automated pass-through.
Speech, labeled with linguistic precision.
Only capabilities Ayta can currently deliver or is genuinely prepared to pilot are listed below.
Transcription
Speech converted to accurate text, including disfluencies and non-speech sounds where your spec calls for it.
Speaker identification
Individual speakers labeled and distinguished across multi-speaker recordings.
Speech classification
Audio segments tagged against your category set for training and filtering pipelines.
Intent
The purpose behind an utterance labeled for voice assistant and dialogue models.
Emotion
Emotional tone labeled in speech where your model needs to detect affect.
Accent / dialect
Regional accent and dialect labeled to support coverage and fairness testing.
Audio segmentation
Recordings split into labeled segments — speech, silence, overlap, or background noise.
Need the source recordings collected first? See Audio Data Collection.
From guidelines to a labeled dataset.
Every annotation program follows the same operating sequence.
Guideline intake
Your taxonomy, edge cases, and labeling rules are built into the task design before annotation starts.
Annotator training
Annotators are trained and qualification-tested against your specific guidelines, not a generic label set.
Labeling
Recordings are transcribed and labeled to spec — speaker, intent, emotion, or segmentation, depending on your task.
QA & calibration
Second-pass review, inter-annotator agreement checks, and dispute resolution run on every batch.
Delivery
Labeled data packaged and delivered in the format your training pipeline expects.
Have a data requirement?
Give us the specification — we'll build the operation around it.
Start a Pilot →