If you are building a large language model, a speech recognition system, or a text-to-speech engine, you need human-generated data. This article explains what AI data collection actually involves and what to look for in a vendor.
Types of AI Data Collection
Speech and Audio Collection
Recruiting native speakers to record scripted or conversational speech in target languages and dialects. Used to train ASR and TTS models.
Handwriting and Image Collection
Collecting handwritten text samples with ground-truth transcriptions for OCR and HTR model training. Particularly challenging in Arabic and Hebrew due to script complexity.
Transcription and Annotation
Converting audio to accurate text, then labeling it with speaker tags, sentiment, intent, toxicity flags or domain categories.
LLM Evaluation (RLHF and Red-Teaming)
Human evaluators rate model responses for quality, helpfulness, accuracy and safety. Used in reinforcement learning from human feedback pipelines.
What Makes a Good Data Collection Partner
- Native speakers, not translators — especially for dialect or cultural nuance tasks
- Multi-tier QA — at minimum, a first-pass transcriber and a separate QA reviewer
- Structured delivery formats — data formatted to your annotation guidelines
- NDA and data security — any responsible vendor signs an NDA before seeing your data
AXOVATE built its entire operation around Arabic and Hebrew — the languages most AI companies struggle with and where high-quality human data has the highest value.