Speech Data Annotation

Speech data annotation is essential for training modern voice based AI systems. Models that perform speech recognition, speaker detection, transcription, or audio driven reasoning require precisely labeled datasets that capture not only what is being said but also how, when, and by whom. DataVLab provides specialized speech annotation workflows for companies building ASR engines, conversational AI, voice assistants, call center analytics, and multimodal LLMs.

Our offering:

Our team annotates speech datasets across multiple dimensions including speaker identity, timestamp segmentation, phonetic structures, language and dialect classification, sentiment, and acoustic conditions. We support monolingual and multilingual corpora, noisy recordings, call center conversations, scripted datasets, and long form natural dialogues.

Speech annotation requires meticulous detail. Accurate time alignment, consistent speaker labeling, and clean segmentation directly affect model performance. Our workflows include multi pass review, internal audits, and project specific guidelines calibrated to each taxonomy. We also help define annotation rules for phoneme level work, emphasis markers, disfluencies, and linguistic features that shape vocal expression.

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Custom:

We adapt to different dataset formats and objectives. Whether training a low latency ASR system, a speaker verification model, or an enterprise voice intelligence solution, our annotators follow standardized quality processes that ensure consistency and reliability across large volumes of audio.