What machine learning model type is characterized by processing sequences of data, such as text or speech?

The model type that is specifically designed to process sequences of data, such as text or speech, is the recurrent neural network (RNN). RNNs have a unique architecture that allows them to maintain a memory of previous inputs through their internal loops, making them highly effective for tasks where the order of input data is crucial. This feature is particularly advantageous in applications like natural language processing or time-series analysis, where understanding context and sequence is essential for generating coherent outputs or predictions.

RNNs are often utilized for tasks involving sequential data because they can take into account not only the current input but also the information from previous inputs in the sequence, allowing them to learn temporal dynamics effectively. This characteristic distinguishes them from other types of neural networks, which may not inherently preserve the sequence or temporal information.

While convolutional neural networks (CNNs) are excellent for processing grid-like data such as images, generative adversarial networks (GANs) are utilized for generating new data samples, and transformer models have gained popularity for sequence tasks as well, RNNs are particularly noted for their suitability in handling ordered data directly through their sequence processing capabilities.