Perfected by humans
Model performance depends on dataset consistency. Automation can introduce subtle errors at scale including segmentation drift, diarisation instability, contextual substitutions, and inconsistent labelling.
ML transcription & annotation service
Machine Transcripts, Refined by Humans for ML-Ready Quality
Human-verified. ML-ready. Trusted. We help AI teams check, correct, standardise, and enrich speech datasets so transcript quality is consistent, traceable, and ready for model development.
Existing client? Log in to your account.
Designed for quality
Built for ML and AI product teams, ASR and conversational AI teams, LLM developers, research labs, and localisation specialists that need model-ready transcripts and documented QA.
Human-Validated, ML-Ready Annotated Datasets
Way With Words delivers ML-ready transcripts through professional human transcription, transcript validation, and structured annotation.
Whether you have raw audio, existing transcripts, or partially labelled data, we help you check, correct, standardise, and enrich your dataset for reliable downstream model performance.
Service Tiers
Tier 1
Dataset Validation
$2.00/audio minute
- Transcript-to-audio alignment and error correction.
- WER reduction with human-verified QA.
- Dense annotation priced by criteria depth and QA scope.
- Volume discounts for large or ongoing datasets up to 20%.
Tier 2
Dataset Curation
$3.25/audio minute
- Verbatim transcription with standard annotation.
- Scoped labelling under defined criteria rules.
- Dense annotation priced by criteria depth and QA scope.
- Volume discounts for large or ongoing datasets up to 20%.
Tier 3
Dataset Enrichment
$5.50/audio minute
- Custom multi-layer annotation architecture.
- Complex criteria design with intensive QA.
- Dense annotation priced by criteria depth and QA scope.
- Volume discounts for large or ongoing datasets up to 20%.
Pricing depends on audio quality, number of speakers, domain complexity, label density, and QA depth. Most teams start with a pilot so scope and quality targets are proven before scaling.
ML Transcription & Annotation Service Key Offerings
1 Create Transcripts From Audio
Create datasets from raw audio
Produce high-quality transcripts from supplied audio.
2 Validate Speech Dataset
Validate your existing dataset
Check and standardise transcripts and labels against audio.
3 Produce Enriched Speech Datasets
Add structured annotation
Apply training-ready labels, tags, and fields.
End-to-End ML Transcription & Annotation Workflow
1
1
Transcription from raw audio
- High-accuracy human transcription aligned to your required conventions.
- Consistent formatting to support downstream annotation and modelling.
- Options for domain-specific handling (meetings, interviews, contact centre, broadcast, research, multilingual).
2
2
Transcript and audio validation
- Transcript verified against source audio, with omissions and substitutions corrected.
- Speaker diarisation reviewed and corrected for consistent attribution.
- Utterance segmentation with timestamp alignment for model-ready outputs.
3
3
Structured annotation
- Intent and classification tagging aligned to your taxonomy.
- Entity recognition, sentiment, and conversational attributes where relevant.
- Custom criteria, safety labels, and export formats aligned to your pipeline.
ML Transcription & Annotation Use Cases
We support teams producing accurate, human-validated speech datasets for training, evaluation, and model refinement across ASR, conversational AI, LLM, and speech analytics programmes.
From correcting machine-generated transcripts to building fully annotated, model-ready corpora, we help ensure data quality, consistency, and scalability for research, enterprise AI deployment, and long-running machine learning programmes.
Use case 1
Improving Existing ASR Datasets
Tier 1 - Dataset Validation
AI teams often have large volumes of machine-generated transcripts but struggle with elevated word error rates, misaligned timestamps, and inconsistent speaker attribution. These issues reduce model training quality and distort evaluation metrics.
We provide transcript-to-audio alignment verification and human error correction to reduce WER and improve dataset integrity, helping teams salvage and strengthen existing corpora without rebuilding from scratch.
Typical users:
- ASR product teams refining acoustic models.
- Enterprises auditing speech datasets before production deployment.
- Research labs validating benchmark datasets.
Use case 2
Building Validated Ground Truth Datasets
Tier 2 - Dataset Curation
Teams training new ASR or speech-to-text models need high-accuracy ground truth transcripts from raw audio. Inconsistent transcription methods and light QA often lead to unstable training outcomes.
We produce verbatim transcription with multi-pass human validation and optional predefined annotation layers, delivering standardised, training-ready datasets aligned to your schema rules.
Typical users:
- AI startups training proprietary ASR models.
- LLM teams building speech-enabled applications.
- Voice interface and conversational AI developers.
Use case 3
Developing Complex Annotated Training Corpora
Tier 3 - Dataset Enrichment
Advanced machine learning systems require multi-layer annotation that captures linguistic, acoustic, semantic, or behavioural signals. Dense labelling needs robust schema architecture and structured adjudication workflows.
We support custom annotation design, high-density labelling, and intensive QA to produce model-ready corpora for supervised learning, intent modelling, sentiment detection, diarisation refinement, and domain adaptation.
Typical users:
- Large enterprise AI divisions.
- NLP model developers requiring structured training inputs.
- Speech analytics platforms developing predictive models.
Frequently Asked Questions
ML Transcription & Annotation FAQs
Do you create datasets from scratch?
Yes. If you supply raw audio, we produce high-quality transcripts as a training-ready foundation. If you already have transcripts or labels, we validate them against audio, correct them, standardise formatting, and add structured annotation as needed.
Can you work with our existing transcripts and just check them?
Yes. Many clients arrive with earlier transcripts from manual or automated workflows. We verify against audio, correct errors, align segmentation and timestamps, and standardise output against your criteria.
Can you add annotation on top of validated transcripts?
Yes. Annotation can be added after transcript stabilisation or run alongside validation, depending on your workflow and schema.
Can you work in our annotation tools?
Yes. We can work within your internal platform (subject to access requirements) or deliver outputs in your preferred export format.
How do you measure quality?
We agree acceptance criteria upfront, then apply sampling, review loops, and correction controls. QA summaries and revision notes can be provided against your criteria.
Can we start small?
Yes. A pilot batch is strongly recommended to validate guidelines, edge cases, and throughput before scaling.
Who has access to my data?
Access is restricted to authorised project personnel operating under confidentiality agreements and controlled access workflows.
How long will my project take?
Timelines depend on volume and complexity. Smaller projects can often be delivered within about a week, while larger volumes are scheduled with agreed milestones.
Ready when you are
Talk to Us
Share your project scope, annotation criteria, and target output format. We can help you run a pilot, define quality targets, and scale production with confidence.
ML Transcription & Annotation quote form
Share your project scope and requirements so we can propose a suitable pilot or production plan.