Multimodal and speech AI training data
Human-generated and human-validated audio, image, and video data, with transcription, annotation, and evaluation labels, used to train and benchmark models that hear, see, and speak. Appen collects and validates this data across languages, accents, and acoustic conditions to defined quality standards.
Speech and audio training data
Speech recognition training data
Read, scripted, and spontaneous speech collected across accents and devices, transcribed to a defined word-error protocol for ASR training.
Conversational speech data
Multi-party, spontaneous dialogue with speaker diarization and turn-level timestamps for real-time voice systems.
Medical speech and clinical conversations
Physician dictation, doctor-patient conversations, telehealth interactions, and medical transcription datasets for ambient scribing, clinical documentation, medical speech recognition, and healthcare AI assistants.
Code-switched and dialectal speech
Audio across niche languages and regional accents in over 500 locales, validated by native speakers.
Multi-speaker audio transcription
Speaker-attributed transcription of overlapping, multi-party audio, scored for inter-annotator agreement.
Acoustic scene and emotion detection
Labeled audio identifying background environment and speaker sentiment for context-aware models.
Paralinguistic event labeling
Categorization of non-verbal cues (laughter, hesitation, sighs) that change spoken intent.
Expressive TTS synthesis
Prosody, emotion, and breath-marker annotation on scripted and spontaneous speech, teaching synthesis models to sound naturally human rather than merely intelligible
Multimodal AI training data
Visual context extraction
Dense captioning, object-relationship mapping, and scene description for vision-language models.
Video action and intent recognition
Temporal sequence labeling that helps models predict the next human action.
Agentic voice UX evaluation
Human evaluation of voice-agent naturalness, task success, and interruption handling.
Low-latency dialogue flows
Training data for sub-second interruption handling in real-time voice systems.
Translation and internationalization evaluation
Fluency, adequacy, and cultural-adaptation scoring across languages and dialects.
Medical imaging and multimodal reasoning
Annotation and evaluation data spanning medical images, clinical reports, and contextual metadata to train and benchmark multimodal models that reason across visual and textual healthcare information.
In-cabin automotive intelligence
Multimodal voice, gaze, and gesture data for driver monitoring and the intelligent cockpit.
How we build and validate the data
Generic annotators fall short as enterprise agentic deployment accelerates. Appen's vetted specialists give research teams domain-authentic data, annotation, and evaluation, deployed quickly.
Contributor network: A global crowd spanning over 500 locales, recruited and vetted for native fluency and domain fit. Domain-specific programs support healthcare, finance, automotive, legal, and enterprise AI use cases, matching contributors and annotators to the expertise required for each project.
Quality measurement: Multi-pass validation with inter-annotator agreement (IAA) thresholds, gold-set audits, and native-speaker review, reported per deliverable.
Acoustic and technical standards: Phonetically balanced scripts, controlled and in-the-wild capture, 48kHz audio, ASR-ready labeling.
Governance: Consent-based collection and documented data provenance for licensing and compliance review.
Track record: 30 years of data operations for foundation-model and enterprise teams.
Ready-to-use datasets
English (US) call centre audio — Transport
Real-world contact-centre calls in stereo, with each speaker on a separate channel for diarization and multi-speaker transcription.
English medical dictation audio + doctor SOAP reports
Physician dictation paired with the original doctor-authored SOAP reports, for medical ASR, clinical NLP, and documentation automation.
English (UK) TTS female scripted microphone
Studio voice-talent recording with orthographic transcription, phoneme segmentation, pitch marks, and a pronunciation lexicon.
Action videos
Participants recording prompted human and animal actions, for temporal action and intent recognition.
Hand gesture videos
Around 11 hours of prompted hand gestures with gesture-type metadata, framed hand-only or with the participant's face.
English (US) product labels
Phone-captured product packaging annotated with bounding boxes plus brand, category, packaging type and image quality.
Case Studies
Dialpad: real-time business-dialogue transcription
Transcription and NLP training data for in-house speech models, lifting labeler accuracy to 88% within weeks and holding it as model diversity grew.
Onfido: secure on-premise labeling for biometric data
A custom on-premise labeling tool kept biometric PII inside their environment while improving fraud detection tenfold across images, video and documents.
How Nearmap scaled AI data labeling for aerial imagery
Aerial image and 3D annotation scaled from a five-analyst pilot to over 180 specialists, delivered against a 180,000-hour annual commitment.
Multilingual image generation in 24 languages.
Prompts localized into 24 languages, then scored by native expert reviewers for cultural relevance, prompt adherence and design quality.
Frequently asked questions
What's the difference between speech data and multimodal AI training data?
Which languages and accents do you cover?
How do you measure annotation quality?
Can I license off-the-shelf datasets instead of a custom collection?
What are typical timelines for a custom collection?
Do you support healthcare and medical AI training?
Ready to train multimodal and speech AI with confidence?
Talk to our team about multimodal and speech AI training data, from vision-language model alignment to audio-visual synchronisation at scale.