Voice recognition using neural networks is becoming more and more common but how exactly does this work? Artificial neural networks are computer programs that attempt to replicate the biological function of the human brain. Neural networks are excellent classification systems and learn through exposure to large amounts of sample data. Neural networks are comprised of an input layer where the user inputs data, one or more hidden layers that translate that data, and an output layer where the user receives their results.
The problem with neural networks and most machine learning algorithms is that they are stateless. This means that whenever a user enters data or numbers through the network, they will always receive the same result and the algorithm will have no memory of previous entries.
A recurrent neural network, also known as an RNN, on the other hand, is a slightly altered version of a regular neural network. This means that when a user inputs data or numbers through the network, the network will take into consideration any numbers the user previously entered. RNNs can use their internal memory, also known as its state, to process sequences of inputs making them applicable to tasks such as handwriting recognition or speech recognition.
Voice recognition using neural networks is so beneficial because it not only makes speech translation and voice recognition more accurate but also improves the time it takes for machine learning algorithms to recognize speech and properly translate it. Voice recognition using neural networks is changing the way humans communicate and is much faster than the written word, allows users to spell better, offers users a hands-free capability, and offers many helpful benefits to people with mental or physical disabilities. Now that you know a little bit more about neural networks, let’s take a look at MATLAB and how they’re improving speech recognition using neural networks MATLAB code.
Speech Recognition Using Neural Networks MATLAB Code
MATLAB, a multi-paradigm numerical computing environment and proprietary programming language developed by MathWorks, uses deep learning algorithms to detect the presence of speech commands through verbal cues. Speech recognition using neural networks MATLAB code trains intricate neural networks to recognize a given set of commands making it easier for the user to receive their results every time they use the network.
Keras Speech Recognition Example
Keras, an open-source neural network library written in Python and capable of running on top of TensorFlow, Microsoft, Cognitive Toolkit, and others, is designed to enable fast experimentation with deep neural networks and focuses on being extensible, modular, and user-friendly. With Keras, users can build a deep learning model to classify words, process data, and train their model to not only pick up verbal cues more easily but provide more accurate results as well.
Voice recognition using neural networks is not a new practice but as technology has become more sophisticated and deep learning algorithms have become more accurate, neural networks are the primary means for users to explore voice recognition. As our society continues to advance deep learning and neural networks, we can expect to see even more accurate, faster voice recognition and speech translation methods.