Liam Steadman

Accuracy Team Lead

Lifting the lid on how Speechmatics' speech recognition learnt the Persian language.

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Understanding Persian - bringing a new language to Speechmatics

Technical

Will Williams

Chief Technology Officer

Charting the Course: Speechmatics' Journey in Advancing Speech Technology

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Building Speech Intelligence on Solid Foundations

Capturing the spoken word - how ASR and AI are transforming media content.

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The Future of Media: ASR and Speech Intelligence

Benedetta Cevoli

Senior Machine Learning Engineer

YouTube’s automated captioning service is notoriously unreliable and represents the dire need for innovation within the speech-to-text industry. Find out what we’re doing about it.

YouTube’s automated captioning service is notoriously unreliable and represents the dire need for innovation within the speech-to-text industry. Click to see what we’re doing about it.

YouTube’s Captions Represent the Dire Need for Speech-to-Text Innovation

YouTube’s Captions Represent the Direct Need for Speech-to-Text Innovation

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Editorial team

Explore sample efficiency in the speech recognition industry. Discover cutting-edge techniques and benefits across industries. Unlock the potential of ASR.

Cambridge Science and Tech Wide Open Day

Sample Efficiency and What it Could Mean for Industry

Adam Walford

Senior Site Reliability Engineer

Speechmatics has recently switched from CPUs to GPUs to run most batch transcription models. Better hardware = increased accuracy. Find out more!

GPU Transcription Models

Autoscaling with GPU Transcription models

Headshot of Data Engineer, Steve Kingsley

Steve Kingsley

Data Engineer

Speechmatics’ ASR system (Ursa) surpasses competitors such as Amazon, Microsoft, and Google in real-time accuracy even when operating at a latency setting as low as 2s, while competitors are set to their highest latency, most accurate configurations. Furthermore, Ursa maintains near batch level performance across different latency requirements.

Best in class ASR system

Best-in-class real-time ASR system

Lawrence Atkins

Machine Learning Engineer

David MacLeod

Machine Learning Architect

Quantizing neural networks offers the promise of improved performance and efficiency, but achieving those gains on GPUs can be hard! This blog explains why, and shares solutions that we use in practice.

Fast and Accurate GPU Quantization for Transformers

Caroline Dockes

Speechmatics are proud to announce our real-time voice translation service. Combining our existing best-in-class speech-to-text allows us to offer highly accurate real-time translation, in our single speech API. Try it out today!

Real-Time Translation

Introducing Real-Time Translation: Breaking Down Language Barriers

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Our latest Ursa release was able to achieve incredible accuracy partly through scaling self-supervised learning. In this blog we demonstrate the power of self-supervised learning and challenge the assumption that scaling labeled data is the key to greater accuracy. We show that with 300x less the amount of labeled data we still beat the nearest vendor by 12% relative.

Self-Supervised Learning

Boosting sample efficiency through Self-Supervised Learning

Why predicting durations as well as tokens allows transducer models to skip frames and achieve up to 2.82X faster inference.

How Nvidia Dominates the HuggingFace Leaderboards in This Key Metric

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