Speaker diarization
Identify and separate different speakers in an audio file to support call analysis, meeting transcription, conversational AI, and speech model training.
Audio annotation
Structured sound labels for speech and audio AI.
Transform raw recordings into searchable, model-ready data through speaker, language, event, noise, intent and timestamp annotation.
What we annotate
Speech, speakers and sound events made measurable.
We align labels, timing precision and acoustic categories with your speech, safety or environmental audio task.
Audio evaluation
Review audio quality, label accuracy, speech clarity, and annotation consistency to ensure datasets meet training and evaluation requirements.
Offensive language identification
Detect and label offensive, abusive, harmful, or inappropriate spoken language to support audio moderation and safety workflows.
Audio classification
Analyze and categorize audio recordings by type, source, environment, or content to help models distinguish speech, music, noise, and other sound events.
Multi-label non-speech audio annotation
Assign multiple labels to overlapping non-speech sounds to help models recognize complex audio environments with mixed sound sources.
Natural language annotation
Tag spoken language data for meaning, sentiment, dialect, intent, and linguistic nuances to support speech and conversational AI systems.
Acoustic noise annotation
Identify and label background sounds, noise types, and acoustic conditions to improve speech recognition and audio model performance in real-world environments.
Linguistic annotation
Label audio data with linguistic metadata such as pronunciation, speech patterns, pauses, emphasis, and language features for machine learning models.
Event tracking and timestamping
Mark the exact time when specific sounds, speaker changes, language shifts, or audio events occur to create structured, searchable audio datasets.
Built for your use case
Audio labels matched to the system you are training.
We define timing, speaker, language and acoustic rules around the decisions your model needs to make.
01ASR & transcription
02Conversational intelligence
03Audio safety & moderation
04Sound event recognition
How we deliver
From audio taxonomy to validated timelines.
A calibrated workflow keeps speaker boundaries, timestamps and acoustic labels consistent across recordings.
Define the schema
Align events, speakers, timing rules, language features, volume and acceptance criteria.
Train & calibrate
Qualify reviewers with practice audio and timing guideline feedback.
Annotate & review
Run controlled production with waveform checks, audits and issue resolution.
Deliver & report
Provide structured labels and timestamps with quality documentation.
Start focused
Plan an audio annotation pilot.
Share your sample recordings, label schema and timing requirements. We’ll propose a focused workflow with clear quality criteria.
Discuss your audio annotation project ↗