Find out how discovery is embedded in our business.
Experience has taught us that to really excel at something, you need world-class expertise at your fingertips.
That's why our core team of researchers is encouraged to iterate across AI, neural networks, machine learning, and language models and apply their learnings to our technology.
We're always looking for opportunities to prototype, expand our offering and provide industry-leading accuracy.
Speechmatics Research Papers
Speechmatics’ paper was submitted and accepted to the most prestigious ML conference – NeurIPs. The paper is about a type of lossy image compression algorithm based on discrete representation learning, leading to a system that can reconstruct images of high-perceptual quality and retain semantically meaningful features despite very high compression rates.Read Our Research Paper
Speechmatics published the paper at NeurIPS 2019 presenting in the meta-learning workshop.Read Our Research Paper
In this paper, Speechmatics demonstrates how you could improve recurrent neural network language models by optimizing for downstream speech recognition accuracy directly, rather than the usual generative approach which tries to model the probability of the next word in a sequence.Read Our Research Paper
Speechmatics published the paper at Workshop on Statistical Machine Translation (WMT) 2018 and presented a translation proof of concept.Read Our Research Paper
At Interspeech 2018 in Hyderabad Speechmatics referred to as one of the most accurate providers of ASR after some evaluations, such as one done by Adobe Research. We demonstrated that our continued focus on innovation and to drive new R&D maintains our position in a growing and increasingly challenging field.Read Our Research Paper
This is the first paper that shows that recurrent net language models scale to give very significant gains in speech recognition and it describes the most powerful models to date and some of the special methods needed to train them.Read Our Research Paper
This paper with Google presents a standard large benchmark so that progress in language modeling may be measured. Prior to this paper there was no open, freely available corpus that was large enough to be representative for modern language modeling tasks.Read Our Research Paper
This paper provides an overview of the 2002 state-of-the-art methods to perform speech recognition using neural networks.Read Our Research Paper
Here we show that speech recognition can be used to find information in audio in much the same way that web pages can be found with a search engine.Read Our Research Paper
Here we fundamentally change the main mechanism in speech recognition to make it both faster and more memory efficient (also US patent 5983180).Read Our Research Paper
This presents the first “end-to-end” training paper for tasks such as speech recognition.Read Our Research Paper
Recurrent nets applied to large vocabulary speech recognition for the first time.Read Our Research Paper
Recurrent nets are demonstrated to give the best performing system on a well-established phoneme recognition task.Read Our Research Paper
The first application of recurrent nets to speech recognition.Read Our Research Paper
This PhD thesis introduces several key concepts of recurrent networks, several different novel architectures, the algorithms needed to train them and applications to speech recognition, coding, and reinforcement learning/game playing.Read Our Research Paper