Speech recognition, the technology powering IVR applications, is constantly evolving.  From wide-release technology like the type manufactured by Nuance to power Siri and Nina, to more abstract features like voice biometrics, there are myriad exciting developments occurring daily within the field that may prove to revolutionize computing on the whole.

One of the more abstract developments within the field of IVR and speech recognition is the cultivation of neural networks that have deep learning capabilities.  A neural network is composed of artificial neurons or nodes, and per Wikipedia, is used to solve artificial intelligence problems.  A mathematical or computational model is deployed to enable computation and data modeling, and is most commonly used for data processing.

Programmers developing neural networks within artificial intelligence systems deploy the technology with the intent of training a program to behave in much the same way as the human brain would.  The end goal is to solve problems or facilitate data computation via a desired pattern.

The problem programmers often run into within these neural networks is the amount time, energy, data manipulation and network alteration required to fine tune the network’s performance and produce the desired results without large amounts of grooming.

Per an article in I-Programmer by Alex Armstrong, training systems in this manner required “big data – lot’s of it.”  Finding and storing enough data to successfully complete this task was incredibly difficult, and without this data the neural network couldn’t refine its processes to train itself to properly construct a high functioning application.

In addition to the big data problem was the fact that networks require a large amount of computing power to facilitate this type of learning.  Most people with onsite applications simply didn’t have the capacity to accommodate the intense type of processing power required for this type of functionality.  The growth of cloud computing has helped to assuage that issue.

So the result of all this?  Per i-Programmer, computer-based neural networks are beginning to function in a highly efficient and effective manner, performing functions in ways they were previously unable to.

With successfully operational neural networks in place, programmers and developers can turn their attention to facilitating deep learning, especially via speech recognition.  So what is deep learning and how does it affect neural networks?

Stay tuned for Deep Learning…