Hi, this is Wayne again with a topic “Voice Recognition As Fast As Possible”.
So when I was a kid having a conversation with your computer went a little something like this, you would lie down on the couch and vent about all the problems in your life or whatever. Then you’d have to get up type. Your own response press Enter and listen back to the insights that it had to offer pathetic right, fast-forward, 25 years and voice-recognition a technology kind of like VR, where someone is always claiming that they’ve made a big breakthrough and it turns out to still be kind of Crap is finally gaining widespread adoption, so early forms of voice recognition had very limited vocabularies. Some of the first systems from 1950s could only recognize about 10 words and even about 30 years later, that number had grown to only around 20,000, which may seem like a lot, but remember the English language has over 1 million words on top of that early software. Couldn’T predict what words you were trying to say by using context so to these programs.
It was just as likely that you were trying to say bacon legs as bacon and eggs. Fortunately, though, we finally got computers in the late 1980s and early 1990s that had more storage and processing power, allowing them to comprehend more natural speech. Instead of forcing you to talk like this, so they could understand, and nowadays, technologies like Siri and Cortana, don’t rely on a limited dictionary or the relatively weak processor in your device at all. Instead, they use huge cloud databases that store millions of words and phrases and have lightning-fast CPUs to understand what you mean with much more precision Google speech recognition.
Software even learns from real search engine strings and can also recognize a variety of accents. So you can use it whether you’re from Eastern Canada or southern Texas, but can the power of the cloud do more when you ask your phone for a sports score, how does it know to tell you how your favorite team is doing instead of where to find A spork store well instead of listening to just one word at a time – and I alluded to this before it listens, to other words as well for context and uses probabilities to determine what you’re trying to say. This is a pretty involved process that uses complex mathematical models. Google, for example, uses an artificial neural network that functions similarly to your own brain, using digital neurons.
To learn what people are saying. You can actually see this in action, as Google now often changes what it thinks you said on the fly, as you continue speaking and it gets better. Greater amounts of processing power have enabled everything from real-time translation, to being able to talk to gain characters with a VR headset to emotion, tracking, in which a computer can use the timing and pitch of your voice to figure out how you’re feeling we’re even seeing it Deployed in fighter aircraft, so pilots can concentrate on mission objectives instead of fiddling with cockpit switches, but although voice recognition has come a really long way, its proliferation has presented us with some new challenges. One big concern has been finding ways to filter out background noise, so you’ll still get correct results, even if you’re standing in the middle of a busy street and speaking of standing in public. Another massive issue is privacy. Many types of voice recognition software improve upon themselves by learning user habits and combined with cloud processing. We’Ve already seen some real concerns, such as with Samsung Smart TVs earlier this year, which had a privacy policy which some people believed allowed Samsung to monitor your living room. Conversations so while the tech has great potential, we also need to make sure it will leave us all feeling, like Winston Smith from 1984 and on the subject of 1984.
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