Speech Annotation

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.

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. We handle diverse audio sources such as call recordings, meeting audio, podcasts, voice notes, smart device commands, and in-car speech. We can also work with multimodal inputs when audio is paired with metadata or timestamps from applications and devices, so your model learns from realistic production signals.

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To keep datasets consistent across languages and accents, we align the labeling schema to your objectives and target environment. This includes guidance on text normalization (numbers, abbreviations, punctuation), language and dialect rules, background noise handling, and edge cases such as interruptions, crosstalk, and low quality recordings.

Quality in speech datasets comes from repeatable guidelines and measurable checks. We set up validation rules early, then run multi pass review with targeted sampling. This helps reduce label noise on the hardest cases, such as short utterances, overlapping speakers, ambiguous intent, and inconsistent punctuation or normalization.

Our QA process typically includes calibration rounds, ongoing audits, and consistency tracking over time. If you have ground truth or a benchmark subset, we can maintain a gold set for monitoring accuracy and drift during production labeling.