John Hughes

Accuracy Team Lead

Accuracy Team Lead, John Hughes, explains how and why Word Error Rate as an accuracy measure is outdated and often misaligned with human judgment.

 In part one of a two-part piece, Accuracy Team Lead, John Hughes, explains how and why Word Error Rate as an accuracy measure is outdated and often misaligned with human judgment. Part Two looks at where we go next.

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The Problem with Word Error Rate (WER)

Technical

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Editorial team

Join the Speechmatics team as we take a look back on all we’ve achieved in the past twelve months – from accuracy uplifts to a host of new features... and much, much more. 

2022 – A Year at Speechmatics

Company

Caroline Dockes

Machine Learning Engineer

Join Machine Learning Engineer, Caroline Dockes, as she explains the latest series of experiments aimed at moving speech recognition away from word-level vocabulary and towards subwords. 

Recognizing Rare Words: Experiments with Subword Units

Katy Wigdahl

Our CEO, Katy Wigdahl, looks at the new AI Bill of Rights and what it means for companies with AI at the heart of everything they do.

What Does the AI Bill of Rights Mean for Tech Companies?

Ellena Reid

Join Speechmatics Machine Learning Engineer, Ellena Reid, as she looks at the best way to turn a single node training setup into a robust, platform-agnostic, multinodal one. 

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How to Successfully Achieve Multinode Training in PyTorch

Oliver Parish

  Machine Learning Engineer

Join Machine Learning Research Engineer, Oliver Parish, and Accuracy Team Lead, John Hughes as they look for ways to boost RNNT accuracy in speech-to-text using external Language Models.

How to Effectively Introduce External Language Models in the RNNT by Subtracting Internal Language Model Scores

Paul Gordon

Product Marketing Manager

Join Speechmatics’ Product Marketing Manager, Paul Gordon, as he looks at our latest product release, including the introduction of new languages, the improvement of others and the release of our Real-Time SaaS.

Product Release November 2022: Including Language Coverage for over Half the World’s Population

Product

Owen O-Loan

Director of Engineering Operations

Join Product Manager, Owen O’Loan as he takes a deep dive into Speechmatics all-new Real-Time SaaS offering, where accuracy comes hand-in-hand with speed.

RT SaaS API

Introducing Real-Time SaaS: Balancing Accuracy with Speed

Accuracy Team Lead, John Hughes, explains how we can move beyond Word Error Rate (WER) and looks for a better solution. 

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The Future of Word Error Rate

The Future of Word Error Rate (WER)

Will Williams

Chief Technology Officer

Lawrence Atkins

Whisper Speech to Text presents an exciting opportunity for the AI community but also leaves us with a lot of questions.

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Whisper Speech to Text Deep-Dive

Oak is Speechmatics’ new medical speech-to-text model, built for clinical conversations that move between languages and speakers.

Meet Oak: medical transcription that follows the conversation across languages and speakers

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We are Speechmatics. Global experts in deep learning and voice technology. Accurate speech-to-text for analysis and accessibility.

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Global experts in deep learning and voice technology. Accurate speech-to-text for analysis and accessibility.

Stay up-to-date with the very latest product news, company news and artificial intelligence (AI) research. Bookmark our technology blog today!

Blog & Latest Speech Recognition News| Speechmatics

Discover how medical transcription improves clinician productivity by reducing documentation time, streamlining workflows, and supporting patient care.

How Medical Transcription Supports Clinician Productivity

General

As healthcare systems face growing pressure, multilingual AI could help clinicians navigate language barriers and spend more time with patients.

Healthcare is feeling the strain. Multilingual AI can be the answer. 

Use Cases

Ten questions to Stuart Wood, Director of Product, on the economics of speech at scale, how close local accuracy now gets to cloud, and who could benefit from Speechmatics On-Device.

On-device speech-to-text, in 10 questions

Learn how law firms can reduce deposition costs with AI transcription by saving time, lowering expenses, improving efficiency, and streamlining workflows.

How Law Firms Can Reduce Deposition Costs with AI Transcription

Latest

Quantization was the key to fitting a cloud-grade model on a laptop. Getting the full optimization chain to cooperate around it was the hard part.

Speechmatics versus Whisper: how Adobe Premiere's on-device speech engine got rebuilt

Adobe Premiere users can run the most accurate on-device transcription locally; efficient enough for a laptop, powerful enough for professional work.

Adobe and Speechmatics deliver cloud-grade speech recognition on-device for Premiere

Turning real-time clinical speech into trusted, EHR-native automation.

One word changes everything: Speechmatics and Edvak EHR partner to make voice AI safe for clinical automation at scale

The metrics, milestones, and real-world outcomes that defined Voice AI’s breakout year.

Speechmatics in 2025: the numbers that shaped Voice AI's breakthrough year (+ what’s to come in 2026) 

Agent STT, powered by Linden 1, gives voice agents the speed, accuracy and conversational context they need in production.

Introducing Agent STT: built for the words that cost you the call

Scale more easily with Speechmatics 

Linden, Speechmatics' new speech-to-text model built for voice agents, is now available through LiveKit Inference, no separate API key, account, or invoice required.

Speechmatics & LiveKit Inference target the accuracy gap that breaks voice agents in production

Speechmatics is now live on Zapier. Connect industry-leading speech-to-text to 8,000+ apps with no code.

Speechmatics on Zapier: No-Code Speech-to-Text Automation

With 93% accuracy, our new model is twice as good as the nearest competitor.

Speechmatics launches Medical Model for real-time clinical transcription

Because in the real world, conversations are messy, and Voice AI needs to keep up.

Speaker Focus: Fixing Voice AI for the real world

A multilingual speech-to-text model from Speechmatics, with code-switching across [<langcount>] languages. Available today in production preview, starting with batch transcription.

Introducing Melia, our new multilingual speech-to-text model

A guide for voice AI engineers, ecommerce platforms and warehouse teams on SKU recognition accuracy voice assistant deployments depend on: why speech recognition systems produce transcription errors on product codes, what to measure when error rates matter, and the fixes that move the needle on order picking, voice ordering and customer-facing voice AI.

Alphanumeric speech recognition: why voice assistants mangle SKUs (and how to fix it)

A new integration gives agent developers a QA layer built for the complexity of the real world.

Speechmatics and Cekura bring real-world STT testing to voice agent pipelines

On Arabic and English, Melia 1 runs at less than half the mixed error rate of the next best model. On Mandarin and Tamil it switches more accurately than anything else we tested.

Melia 1 leads speech-to-text code-switching in Arabic, Mandarin and Tamil

An hour of audio in, a transcript back in under 20 seconds. 30 minutes in under 10 seconds. 10 minutes in under 5 seconds.

Gone in 20 seconds: Melia outpaces AssemblyAI, ElevenLabs and Soniox

Behind the build: what it takes to make cloud-grade speech recognition work inside Adobe Premiere, and why Whisper raised the stakes.

The Adobe story: How we made cloud-grade AI work on your laptop

Voice agents that pass in demos routinely fail in production. This guide covers the 11 best voice agent testing platforms in 2026, with the Five-Layer Testing Framework, platform deep dives, open-source alternatives, and a decision guide by maturity stage.

De-risk your voice agent: The 11 best voice agent testing platforms in 2026

End customers will only ever see their own provider's brand, which means the layer underneath has to be good enough to go unnoticed.

Tresic selects Speechmatics to power the speech layer of its conversation intelligence platform

How Wellcom Health uses real-time transcription, medical-grade accuracy and diarization to turn messy Dutch consultations into validated clinical reports.

Dutch doctors spend a quarter of their day on admin. Wellcom has built the fix.

Speech-to-text has moved from novelty to enterprise infrastructure. Here's how the leading platforms stack up in 2026 — and how to pick the right one.

Best speech-to-text AI guide: APIs, platforms and services compared

The court reporter shortage is reshaping litigation. Explore data, causes, and how legal teams are using digital reporting and AI transcription to adapt.

The court reporter shortage crisis: data, causes, and what legal teams are doing about it

Word error rate for legal transcription has no single acceptable threshold. But knowing how accuracy, audio quality, and review obligations connect to real legal risk is what separates a reliable transcript from a costly one.

What Word Error Rate Is Acceptable for Legal Transcription?

As clinical workflows become automated and AI-driven, real-time speech is shifting from a transcription feature to the foundational intelligence layer inside modern EHR systems.

Why AI-native EHR platforms will treat speech as core infrastructure in 2026

What teams deploying Voice AI at scale see coming next as production pressure reshapes adoption.

7 Voice AI predictions from teams building at scale in 2026

Voice AI moved fast. Regulation caught up. Here’s how to stay compliant in 2026.

Your essential 2026 guide to voice ai compliance in today's digital landscape

Faceless YouTube channels are becoming a scalable, system-driven way for businesses to produce high-impact video without relying on on-camera talent.