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In this Q&A, David Keene, chief marketing officer at Speechmatics, discusses the importance of diversity and inclusion in tackling AI bias, and the value of speech recognition tech

The speech recognition provider was found to be outperforming the likes of Google and Amazon in voice understanding.

This week, Cambridge-based AI speech recognition provider Speechmatics launched its Autonomous Speech Recognition software. The companys technology was found to outperform Amazon and Google in overall accuracy for African American voices (82.8% versus Googles 68.7% and Amazons 68.6%), based on datasets used in Stanfords Racial Disparities in Speech Recognition study. This equates to a 45% reduction in speech recognition errors the equivalent of three words in an average sentence and Speechmatics new software looks to deliver similar improvements in accuracy across accents, dialects, age, and other sociodemographic characteristics.

Up to now, speech recognition has been commonly misconceived due to the limited amount of labelled data available to train on. But in this Q&A, Speechmatics CMO David Keene explained to Information Age the value that the technology can bring, and the importance of diversity and inclusion in tech.

The innovation and adoption of AI technologies is gathering speed at an unprecedented pace. From government AI strategies to the NATO announcement today, this tech is going to be front and centre on the agenda for years to come. For AI technology to be truly useful to the world at large, however, it has to be globally representative. We cannot and must not build AI systems for an elite set of users. It is unethical but also doesnt make commercial sense.

Our machine learning breakthrough has taken a big step forward towards understanding every voice allowing us to plug in to the internet and train on millions of hours of publicly available data rather than smaller, biased labelled datasets. Next step in this journey is to work out how we can understand the digitally excluded those voices that are not commonplace on the internet in audio books, on podcasts and social media networks.

This article will explore why a lack of diversity in tech remains a problem for organisations, despite efforts being made to mitigate this. Read here

In an ideal world, your tech team would mirror the market you are selling to and we have to do better as a community going beyond the cookie cutter hiring process to find those people. That is going to take years and years to achieve though and there are things we can do in the meantime. Inclusion is a mindset and needs to be ingrained into the culture of the business and mapped to the bottom line.

Strength and innovation doesnt come from homogeneity. It is fascinating to see how much tech skews to the make-up of the tech team developing it. Male-heavy developer teams will build tech that works better for men. Tech teams based in Michigan will better understand voices from Michigan (I am looking at you Bing). We need to recognise that we naturally build for our own and make a conscious decision to test innovations with a much broader group of people.

Speech recognition technology is in the fabric of so much of what we do these days. From e-learning to voice assistants, courtroom transcriptions to driverless cars research varies but we are looking at a $30+ billion market within the next few years which is hugely exciting.

That growth is running alongside a macro-move to productivity requiring us to take low value tasks out of the supply chain driving automation and robotics. This all only works positively for wider society if these speech recognition systems understand all voices.

Take McDonalds as an example if they want to put in a speech recognition system to take orders in their drive-throughs that system HAS to understand all its customers. For that to happen the system needs to be trained to understand all voices which means going way beyond the bias labelled datasets that are often limited in terms of representation.

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Automatic is when the machine is fed specific, usually biased human-labelled information to learn on. Autonomous means you plug it in and it learns unsupervised from all available data on the internet. In AI we call this learning on first principles rather than being rules-led. This is the general move that AI innovators are now trying to make. A similar comparison is IBMs Deep Blue vs Googles AlphaZero. Deep Blue was trained on human data from chess games played by people (specific biased data assuming humans know how to play chess). It was trained to beat a human. AlphaZero was trained from first principles to play a superhuman game of chess. We now have the technology breakthrough to do this for something more complex than a game with rules and that is, of course, speech recognition.

Excerpt from:
Q&A: How Speechmatics is leading the way in tackling AI bias and improving inclusion - Information Age