Audio feedback doesn’t give the user the same reassuring sense of certainty as a graphical user interface. One glance wi

Technology to separate speech from different speakers is coming on in leaps and bounds. This is achieved both by analysing the speech more deeply, and by combining the audio data with other sources, like using multiple microphones to measure relative volume and direction, or by using input from cameras to add lip movements and facial expressions to the mix.

Finally, early identification of certain serious long-term neurological conditions is possible by monitoring subtle changes to speech. This can be done without hospital visits or even without targeting those who are at risk. Conveniently for all concerned, we all speak into our phones and computers all the time, so it would only be necessary to opt in, and give permission for your voice to be analysed, without compromising confidentiality by being recorded or listened to.

Speech does not only vary by accent, but also by emotional and physical state. When a condition makes someone unintelligible, it is feasible not only to improve intelligibility but to identify what is wrong, perhaps categorising emergency calls, where the speaker is affected by stroke, sedation, drunkenness, concussion, or merely identify that the caller is a child, or speaks a particular language.

Even five years ago, we needed to train systems for each regional accent, but nowadays, Siri copes with Scottish accents, just by training its networks on Scottish people reading known texts, i.e. teaching the networks the various ways in which a word can be pronounced.

And as for errors, as well as Scottish accented YouTube clips, by 2016, there were much less jokey news stories, making claims that this was inherently racist technology. If Microsoft Office only worked for 90% of people, there would be uproar. Did this mean that speech recognition was just a novelty, rather than real product, empowering business etc?

I have spent a lot of time working on systems which analyse accents, mispronunciations and speech impediments. Some people are difficult to understand because they have an unfamiliar accent, or are only just learning a language. We can make it easier to master pronunciation by giving them real-time feedback, but maybe we needn’t bother: morphing accents, and correcting errors in real time are both becoming a reality.

Away from our headsets, speech isn’t really as linear as I have made out. In close proximity to someone speaking, I might whisper a comment to another listener, and still go unheard by anyone else. At a dinner party, I might be involved in more than one conversation at a time, because it is easy, in the 3D space of the real-world, to keep track of who has said what, and to control the volume and direction of my speech to target a specific listener.

Nowadays, the latest developments are routinely shared, and the whole industry takes the latest ideas from Google, NVIDIA, Microsoft and a global community of university researchers, and with their blessing, extends them and applies them in new contexts, adding expertise from their own niche professions.

In 2016, Google came up with a new approach to speech synthesis, using WaveNet, a neural network, which can be trained to generate almost any kind of sound, and then training it with real human speech. Once trained, it can be fed with quite robotic synthesized speech, and then make it sound human.

Computers have made multi-taskers of us all, and sometimes I think that, as an interface, even for interpersonal communications, speech can sometimes set us back: I can be in several text chats at once, but I can’t be on two voice calls. Text and screen interactions have some real advantages, with which speech shouldn’t even try to compete.

However, for speech technology to reach the potential of what it, uniquely, can do well, it still has much further to go. This is good news for the industry, as more and more startups are funded to solve real-world problems, not dealt with by the big players.

Technology has to get as good at listening, and at speaking, as human beings are, and then — in some contexts — get better than we are. Here are a few examples from projects, which I and others have been working on lately.

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lligence, Data Science, and similar technologies advancing rapidly, there is always a pressing need for skilled Python developers. Throughout this article, we talked about the various GitHub repositories which you can use to learn Python, keeping in mind the skill levels of various developers across the globe. Regardless of your experience with Python, we hope you’ll find these GitHub repositories useful in your learning, for which we wish you good luck.

Finally, early identification of certain serious long-term neurological conditions is possible by monitoring subtle changes to speech. This can be done without hospital visits or even without targeting those who are at risk. Conveniently for all concerned, we all speak into our phones and computers all the time, so it would only be necessary to opt in, and give permission for your voice to be analysed, without compromising confidentiality by being recorded or listened to.

However, neural nets really have come to the rescue, especially for this kind of problem. Having enough of the right training data turns out to be much more important than knowledge of the phonetic differences between accents — the network will work out what those differences are.