US-DATAData Annotation
Cabinet
Speaker segmentation splits speech into participant-specific fragments, helping models distinguish speakers, analyze conversations and improve ASR accuracy.
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Speaker segmentation determines who is speaking at each moment. Audio is split into temporal segments with identifiers such as Speaker 1, Operator, Client and other roles.
Separate utterances in calls, interviews and negotiations.
Label meetings, conferences and group discussions.
Assign roles such as operator, client, doctor or patient.
Label fragments where speakers talk simultaneously.
Segmentation defines temporal boundaries and divides speech by participant. Diarization is broader: it detects speech, finds speaker changes, groups segments and handles overlap.
We label segment starts and ends, Speaker IDs, speaker changes, silence, noise, unintelligible fragments and roles. Precision can be set to seconds, milliseconds, words or utterances.
We work with calls, contact centers, interviews, podcasts, lectures, meetings and noisy recordings. Services include temporal annotation, Speaker IDs, roles, pauses and exports for ASR and diarization.
Inconsistent labels reduce accuracy, introduce bias and can make production behavior unreliable.
We create task-specific guidelines and validate every stage so the dataset matches the model architecture and business objective.
Examples of data annotation for machine learning
Full data preparation cycle from raw data to model-ready output
How does US-DATA deliver the results business needs?
We pay close attention to quality. Even the most accurate model will not perform well if the data is labeled with errors.
Our team works under a unified annotation rule system based on multi-level review and consistency control. Every process is adapted to client needs and the specifics of the ML model. The result is a clean dataset ready for training without additional rework.
We understand how data quality affects model training.
Annotation tailored to architecture and project goals.
From pilots to large-scale data volumes.
Control at every stage and transparent metrics.
We handle non-standard and challenging scenarios.
Data exported in the format you need.
Faster model training
Higher accuracy and robustness
Lower retraining costs
Production-ready datasets
Data security and compliance
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* This estimate is not a public offer. Final cost is determined after technical analysis and data review.
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Speaker segmentation divides an audio recording into temporal fragments by participant. US-DATA labels Speaker IDs, turns, pauses, noise and overlapping speech for ASR, diarization and conversation analytics.
We develop annotation guidelines, run pilot labeling and multi-level quality control, then deliver a consistent dataset in the format required by your ML pipeline.
The result is data that is ready to train, validate and deploy machine learning models in real operating conditions.
