Natural Language Processing (NLP)
Data that speaks global languages — collect, transcribe, and annotate speech and text to strengthen voice AI, ASR, and NLU across markets.
Lingual Consultancy’s NLP services help teams collect, annotate, and operationalize high-quality speech and text data to train, evaluate, and continuously improve AI and deep-learning models across multilingual use cases. Offerings span managed voice data collection and audio datasets, human-in-the-loop annotation with audio annotation tools, dataset preparation, and model evaluation workflows tailored to domain and market needs.
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FAQ
Frequently Asked Questions
Voice data collection is recording real human speech to build representative audio datasets for model training. High-quality voice data collection improves Automatic Speech Recognition (ASR) accuracy, accent coverage, and real-world robustness. This data is essential to train AI models that work across multiple languages.
Audio data collection includes scripted and unscripted recordings across scenarios — media, dialogue, discussion, monologue and in-vehicle capture. Methods range from studio sessions and remote crowdsourcing to field recordings and device/IVR logging to ensure diverse and realistic audio training data. These approaches help us collect audio data that supports ASR, NLP, and machine translation systems.
Audio annotation adds structured labels (transcripts, phonemes, speaker diarization, intent/entity tags, timestamps, noise/emotion markers) so recordings become model-ready. Annotation is required for supervised training, evaluation, fine-tuning and any production NLP or ASR deployment.
Transcription converts speech to text (verbatim or cleaned); annotation enriches recordings with labels and metadata (speaker IDs, timestamps, phonetic segments, intents). Lingual Consultancy combines AI-assisted transcription with human annotation to deliver both accurate transcripts and granular labels.
Yes — we capture both controlled scripted prompts for coverage and unscripted conversational speech for natural variability, giving models balanced exposure.
We accept and deliver ML-ready and common audio formats: WAV, FLAC, MP3, OPUS, plus annotation/export formats such as JSON, Kaldi, TFRecord and SRT.
Quality is enforced through multi-stage QA: consensus checks, inter-annotator agreement, linguistic review, spot audits, and bias-aware sampling. Human-in-the-loop workflows and periodic calibration keep transcripts and labels consistent and production-ready.
We use consent-led capture, anonymization, encrypted storage and role-based access for privacy and compliance. Turnaround and pricing depend on dataset scope (languages, sample size, annotation depth, specialized capture). Share project details and we’ll propose a scoped timeline and pricing that fit your requirements.
Partner with Lingual Consultancy to access reliable, ML-ready audio datasets, audio annotation services and transcription workflows that power better ASR, NLU and NLP outcomes. Contact us today to discuss your next project.