ML Transcription & Annotation
Human Validated Dataset Review For Machine Learning & LLM Training
Human Validated, ML Ready Speech Datasets
Way With Words delivers training ready speech datasets 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 so it is consistent, traceable, and fit for model development.
Tier 1
Dataset Repair- Transcript to audio alignment and error correction.
- WER reduction with human verified QA.
- Dense annotation priced by schema depth and QA scope. Volume discounts for large or ongoing datasets.
Tier 2
Dataset Creation- Verbatim transcription with standard annotation.
- Scoped labelling under defined schema rules.
- Dense annotation priced by schema depth and QA scope. Volume discounts for large or ongoing datasets.
Tier 3
Dataset Enrichment- Custom multi layer annotation architecture.
- Complex schema design with intensive QA.
- Dense annotation priced by schema depth and QA scope. Volume discounts for large or ongoing datasets.
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.
Designed for quality
ML and AI product teams, data science and data engineering groups, ASR and conversational AI teams, LLM developers, research labs, localisation and language technology teams, and organisations improving speech models for contact centers, regulated workflows, or multilingual environments.
↓ Explore our use cases below
Perfected by humans
Model performance is shaped by dataset consistency. Automation can introduce subtle errors at scale, such as segmentation drift, diarisation instability, contextual substitutions, and inconsistent labelling. Our managed human workflows across transcription, validation, and structured annotation reduce noise, improve repeatability, and provide quality reporting you can stand behind when training, benchmarking, or shipping production models.
↓ More about Way With Words
Key Service Offerings
Use our service in three ways.
Validate, repair your existing dataset
you already have transcripts or labels and need them checked against audio and standardised.
Create a dataset from raw audio
you supply recorded audio and we produce high quality transcripts as the foundation.
Add structured annotation
we apply your label set and guidelines to produce training ready tags and fields.
Transcript and audio validation
- Transcript verification against source audio
- Correction of omissions, substitutions, and formatting inconsistencies
- Verbatim options, including disfluencies, if required by your training goals
- Speaker diarisation review and correction
- Utterance segmentation and timestamp alignment
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)
Structured annotation for ML and LLM workflows
- Intent and classification tagging
- Sentiment and conversational attributes (where relevant)
- Named entity recognition and structured labelling
- Safety, security, and policy related labelling (based on your definitions)
- Custom fields, schemas, and export formats aligned to your pipeline
Quality assurance and dataset review
- Sampling plans and acceptance criteria agreed upfront
- Cross annotator review and disagreement checks where applicable
- Error pattern reporting and practical recommendations to reduce future drift
- Revision logs and traceable quality controls
Process
A controlled workflow that scales without quality drift.
Scope and framework alignment
We confirm your goals, label set, formatting rules, edge cases, acceptance criteria, and required output formats.
Pilot run
We complete a representative batch to validate instructions, quality targets, and throughput.
Production
Work is completed by trained human language professionals under supervision with documented checkpoints.
QA and adjudication
Multi-layer review and corrections to maintain consistency, with clear escalation rules for edge cases.
Delivery and handover
You receive final outputs plus a QA summary and any optional guideline refinement notes.
Talk to Us
Send us your requirements.
Frequently Asked Questions about our
ML Transcription & Annotation Service
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 can validate them against audio, correct them, standardise formatting, and then add structured annotation if needed.
Can you work with our existing transcripts and just check them?
Yes. Many clients come to us with transcripts produced by earlier workflows or automation. We verify against audio, correct errors, align segmentation and timestamps, and standardise the dataset to your criteria.
Can you add annotation on top of validated transcripts?
Yes. Annotation can be added once transcripts are stable, or alongside validation, depending on your pipeline.
Can you work in our annotation tools?
Yes. If you use an internal annotation platform, our team can work within your environment subject to access and workflow requirements. Alternatively, we can return outputs in your preferred format.
How do you measure quality?
We agree acceptance criteria upfront, then apply sampling, review loops, and correction controls. We provide a QA summary and revision notes aligned to your criteria.
Can we start small?
Yes. A pilot batch is strongly recommended to confirm guidelines, edge cases, and throughput before scaling.
Can we start small?
Yes. A pilot batch is strongly recommended to confirm guidelines, edge cases, and throughput before scaling.
What do you need to quote a pilot?
A small representative sample, your label definitions or criteria, your preferred output format, and any must follow conventions.
Who has access to my data?
Access is restricted to authorised project personnel operating under confidentiality agreements and controlled access workflows. Retention periods can be aligned to your security and compliance requirements.
How do I get my files to you?
We support secure transfer options based on dataset size and your workflow. For large datasets, we coordinate secure delivery or controlled downloads from your chosen environment.
Is pricing different for from scratch transcription versus validating an existing transcript?
Pricing is quoted based on the overall production effort. In some cases, validating and repairing existing transcripts can be comparable to transcription from raw audio, especially where extensive corrections or re segmentation are required. We confirm the most appropriate tier during the pilot.
Do I have to pay upfront?
For projects exceeding 50 hours, a deposit is typically required to initiate production. For smaller engagements, monthly invoicing may be arranged, depending on scope.
How long will my project take to complete?
Timelines depend on volume and complexity. For projects of 50 hours or fewer, a one week turnaround is often achievable. Larger volumes are scheduled and scaled with delivery timelines agreed in advance.
Way With Words
Human-Validated Speech Data for Machine Learning and LLM Training.
At Way With Words, we specialise in producing and validating high-quality speech datasets for machine learning applications. We support AI teams with structured transcription, transcript validation, and schema-based annotation services designed to improve model training accuracy and downstream performance.
While automated systems can generate large volumes of raw transcripts, they often fall short on alignment accuracy, speaker differentiation, domain terminology, and structured labelling. Our ML Transcription & Annotation services address these gaps through professional human review, controlled quality assurance workflows, and scalable annotation support tailored to your schema requirements.
With decades of transcription expertise and established quality frameworks, we work with audio-only datasets as well as existing transcript corpora. Whether correcting errors, reducing word error rates, applying predefined annotation layers, or executing complex multi-dimensional labelling projects, we deliver model-ready datasets aligned to your technical specifications.
This service is built for AI developers, ML engineers, data scientists, research labs, and enterprise teams requiring reliable, human-validated ground truth data at scale.
ML Transcription & Annotation Use Cases
Our ML Transcription & Annotation services support AI teams in producing accurate, human-validated speech datasets for training, evaluation, and model refinement. Organisations developing ASR systems, large language models, conversational AI, and speech analytics platforms rely on our structured transcription, validation, and schema-based annotation workflows to improve dataset integrity and model performance.
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.
Improving Existing ASR Datasets
(Tier 1 – Dataset Repair)
AI teams often possess 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 bias evaluation metrics.
Way With Words provides transcript-to-audio alignment verification and human error correction to reduce WER and improve dataset integrity. This enables teams to salvage and strengthen existing corpora without rebuilding datasets from scratch.
Typical users:
• ASR product teams refining acoustic models
• Enterprises auditing speech datasets before production deployment
• Research labs validating benchmark datasets
Building Validated Ground Truth Datasets
(Tier 2 – Dataset Creation)
Teams training new ASR or speech-to-text models require high-accuracy ground truth transcripts from raw audio. Inconsistent transcription methodology and insufficient QA can lead to unstable training outcomes.
Way With Words produces verbatim transcription with multi-pass human validation and optional predefined annotation layers, delivering standardised, training-ready datasets aligned to defined schema rules.
Typical users:
• AI startups training proprietary ASR models
• LLM teams building speech-enabled applications
• Voice interface and conversational AI developers
Developing Complex Annotated Training Corpora
(Tier 3 – Dataset Enrichment)
Advanced machine learning systems require multi-layer annotation frameworks that capture linguistic, acoustic, semantic, or behavioural signals. Dense or multi-dimensional labelling requires carefully designed schema architecture and structured adjudication workflows.
Way With Words supports custom annotation design, high-density labelling, and intensive QA processes to produce model-ready corpora suitable for supervised learning, intent modelling, sentiment detection, diarisation refinement, or domain adaptation.
Typical users:
• Large enterprise AI divisions
• NLP model developers requiring structured training inputs
• Speech analytics platforms developing predictive models