Ana Olssen

Machine Learning Engineer

Speechmatics' newest model, Ursa, performs better than ever on specialized topics like medicine and finance.

Join Ana Olssen as she explains how we can understand every voice in specialized domains by relying on self supervised learning combined with powerful language models.

Domain Generalization image

Ursa: Scaling up as a Solution to Domain Generalization

Technical

Lawrence Atkins

David MacLeod

Machine Learning Architect

In a world of increasingly costly machine learning model deployments, ensuring accurate GPU operation timing is key to resource optimization. In this blog post, we explore best practices to achieve this in PyTorch.

PyTorch timing operations

How to Accurately Time CUDA Kernels in Pytorch

By combining the output of an automatic speech recognition (ASR) system with a large language model such as GPT-4 and ChatGPT, we can do many downstream tasks in a zero-shot way by including the transcript and task in the prompt.

GPT-4 - What You Need To Know

Accuracy Matters When Using GPT-4 and ChatGPT for Downstream Tasks

John Hughes

Accuracy Team Lead

On March 14 OpenAI released GPT-4, a multi-modal large language model that can accept both images and text as input. Learn how GPT-4 works and what it means in this technical blog.

GPT4 - What is it?

GPT-4: How does it work?

Benedetta Cevoli

Senior Machine Learning Engineer

Our latest release, Ursa, breaks the accessibility barriers in speech technologies by offering ground-breaking accuracy for every voice. Irrespective of accent, dialect, and various demographic factors, Ursa is consistently the most accurate speech-to-text engine on the market with a relative lead of up to 30% compared to Amazon, Google, Microsoft, and OpenAI’s Whisper across the board.

Accessibility blog header image

Achieving Accessibility Through Incredible Accuracy with Ursa

Will Williams

Chief Technology Officer

We are excited to introduce Ursa, the world’s most accurate speech-to-text system. Ursa delivers unprecedented performance across a diverse range of voices. We observe relative accuracy gains of 22% and 25%* versus Microsoft and OpenAI’s Whisper respectively.

Ursa provides the world’s most accurate speech-to-text and is a breakthrough in accessibility, reducing the digital divide for voices that other speech-to-text systems struggle to recognize.

Ursa - header image

Introducing Ursa from Speechmatics

Headshot of Data Engineer, Steve Kingsley

Steve Kingsley

Data Engineer

Speechmatics' Data Engineer, Steve Kingsley, discusses Common Voice and OSCAR, and how their data can help to rapidly train languages.

How to Rapidly Train New Languages Using Common Voice and OSCAR

Speechmatics’ Data Science Engineer, Benedetta Cevoli looks at why speech technology still has some distance to travel in reducing inequality across different languages.  

Speech Technology in a Global World: Reducing Inequalities Across Languages

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.

Problem WER image

The Problem with Word Error Rate (WER)

Caroline Dockes

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

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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