Power your models with precise and human-verified audio datasets. We transcribe and label speech, music, and sound to build smarter AI systems.
Audio annotation is the process of labeling and transcribing audio data to make it understandable for machine learning models. It involves tagging speech, identifying specific sounds, and segmenting audio files based on speakers or events.
Whether it's training a virtual assistant to recognize voice commands or a medical AI to detect lung sounds, high-quality audio annotation is the critical foundation for success.
End-to-end audio labeling solutions for every AI use case
Converting spoken language into highly accurate text, including timestamping and verbatim options for training NLP models.
Identifying and labeling 'who spoke when' in multi-speaker environments like meetings, podcasts, or call center recordings.
Detailed tagging of phonetic sounds and accents, essential for building robust text-to-speech (TTS) systems.
Categorizing audio files based on content, such as music genre detection, mood identification, or sound effect tagging.
Detecting and labeling specific acoustic events like glass breaking, gunshots, or engine noises for security and industrial AI.
Annotating the meaning, intent, and sentiment behind spoken words to improve conversational AI performance.
Native speaker reviewers and multi-pass quality checks ensure every word, pause, and sound event is labeled correctly.
Support for 30+ languages and regional dialects with native annotators for authentic, accent-aware labeling.
From short recordings to hundreds of thousands of audio hours — our infrastructure scales with your project needs.
Specialist annotators with backgrounds in healthcare, legal, finance, and customer service audio for context-accurate labeling.
Parallel annotation pipelines and dedicated teams ensure rapid delivery even on large, complex audio datasets.
ISO 27001, GDPR, and HIPAA compliant. Encrypted transfers and strict NDAs protect every audio file we handle.
High-quality audio annotation accelerates speech AI performance, accuracy, and market readiness
Clean, precisely transcribed training data directly improves automatic speech recognition performance.
Consistent, structured audio labels accelerate model convergence and reduce retraining cycles.
Annotated data across languages and accents enables globally deployable voice AI products.
Voice automation powered by well-trained models lowers call center and support costs significantly.
Emotion-aware and intent-trained models deliver more natural, responsive voice interactions.
Quality-assured audio data accelerates development timelines for voice products and assistants.
Real-world AI applications powered by expert audio labeling
Train wake-word detection and command recognition models for Alexa, Google, and custom voice assistants.
Enable AI to transcribe, analyze sentiment, and route calls based on speaker intent and emotion.
Annotate physician dictations and patient conversations to power accurate medical transcription tools.
Build robust automotive voice interfaces with accent-diverse, noise-aware annotated speech data.
Train voice biometric models that authenticate users or flag suspicious callers in financial and security apps.
Annotate pronunciation, fluency, and accent data to power AI tutors that give real-time learner feedback.
Tailored acoustic data solutions for specialized domains
Structured workflow for high-fidelity acoustic datasets
Secure ingestion of raw audio files in your preferred format.
Native linguists label and transcribe based on strict guidelines.
Rigorous multi-stage validation to ensure 99% accuracy.
Final polishing and consistency checks across the dataset.
Secure export of labels in JSON, XML, or custom formats.
Everything you need to know about our audio annotation services
"Ours Global transcribed and emotion-tagged over 200,000 call center recordings for us. The accuracy was outstanding and the turnaround exceeded our expectations."
VP of AI, ContactIQ Solutions
"Their multilingual annotation team handled our 15-language dataset flawlessly. Native speaker review made a real difference in our ASR model's real-world performance."
Head of NLP, LinguaTech GmbH
"We needed clinical audio annotation with strict HIPAA compliance. Ours Global delivered with precision and total data security — exactly what healthcare AI demands."
CTO, MediVoice AI