Transcription & segmentation
Verbatim transcripts, timestamps, utterance boundaries, non-speech events, and normalization according to your guidelines.
SarDev Voice Lab
Speech data your GenAI voice systems can trust.
Human-led audio annotation for teams training, tuning, and evaluating speech-enabled AI. We turn raw recordings and model outputs into consistent, reviewable data.
Speech-first workflowsCalibration before scaleEvidence-ready delivery
What we annotate
Audio labeling that follows your model requirements.
We align the rubric, label the edge cases, and deliver structured speech data your team can use for training, evaluation, and product decisions.
Diarization & attributes
Speaker turns, overlap, accents, vocal delivery, intent, emotion, and other requested acoustic or linguistic labels.
Speech evaluation & QA
Human review of pronunciation, intelligibility, audio artifacts, instruction adherence, and label consistency.
A practical annotation workflow
Clear guidelines. Consistent judgments. Usable data.
We start with a representative sample, calibrate the decisions that matter, and refine the rubric before scaling the work.
- Client-supplied taxonomy and output schema
- Sample calibration and exception handling
- Review of ambiguous or low-quality audio
- Structured handoff for training or evaluation
Built for voice AI
Support across the speech model lifecycle.
Bring us recordings, generated speech, or model outputs. We can help define the annotation scope for:
- Automatic speech recognition datasets
- Text-to-speech and conversational voice evaluation
- Call audio and dialogue segmentation
- Pronunciation and speech quality review
Scope, languages, turnaround, security handling, and capacity are agreed for each project.
Start a conversation
Contact SarDevTell us what your audio needs to teach your model.
Share your data type, labeling requirements, volume, languages, and timeline. We’ll discuss a suitable scope and delivery plan.