Voice recognition, also known as automatic speech recognition (ASR), is primarily based on supervised machine learning. This type of machine learning uses labeled data to train algorithms to classify data or predict outcomes accurately. In the context of voice recognition, the system is trained with a large amount of audio samples and corresponding transcriptions.
The most common techniques used in voice recognition include Hidden Markov Models (HMMs), Gaussian Mixture Models (GMMs), and Deep Neural Networks (DNNs).
Hidden Markov Models are statistical models that assume there is an underlying process that generates observable data. They are particularly useful for dealing with time series data like audio.
Gaussian Mixture Models are used for representing normally distributed subpopulations within an overall population. In voice recognition, they help in modeling different classes of speech sounds.
Deep Neural Networks are a type of artificial neural network with multiple layers between the input and output layers which can model complex patterns effectively. They have been increasingly used in recent years due to their high accuracy.
In addition, reinforcement learning can also be used in voice recognition where the system learns by interacting with its environment and receiving rewards or penalties.
It's important to note that these systems often use a combination of these techniques to achieve optimal results. For example, Google's voice recognition system uses a combination of deep learning models, including recurrent neural networks (RNNs) and convolutional neural networks (CNNs).