A neural network is a core concept in artificial intelligence that mirrors, in simplified form, how biological neural networks transmit signals between neurons. Rather than following hard-coded rules, a neural network learns to perform tasks by analyzing large volumes of data and adjusting internal parameters called weights.
How a Neural Network Works
At its simplest, an artificial neural network consists of three types of layers:
Input layer: Receives raw data, such as audio samples, pixel values, or text tokens.
Hidden layers: One or more intermediate layers where computation happens. Each node applies a mathematical function to its inputs, multiplies by learned weights, and passes the result forward. Adding more hidden layers creates what is known as a deep neural network, the basis of deep learning.
Output layer: Produces the final prediction or classification, such as a transcribed word in speech recognition or a sentiment label in text analysis.
The Neural Network Algorithm: Training and Inference
Training a neural network involves two key steps repeated over many iterations. During forward propagation, data flows through the network to generate a prediction. A loss function then measures how far that prediction is from the correct answer. During backpropagation, the network adjusts its weights to reduce the error, using an optimization method such as gradient descent. Once trained, the network performs inference, processing new, unseen inputs to deliver results in real time.
Common Types of Neural Networks
Feedforward neural networks: The simplest architecture, where data moves in one direction from input to output. Useful for straightforward classification tasks.
Convolutional neural networks (CNNs): Designed for grid-like data such as images. They use filters to detect spatial features like edges and textures.
Recurrent neural networks (RNNs): Built for sequential data such as speech or text. They maintain a form of memory that captures context over time, making them well suited for language modeling and audio processing.
Transformers: A newer architecture that uses self-attention mechanisms to process sequences in parallel, powering large language models and modern speech-to-text systems.
Neural Networks in Speech and Voice AI
In voice AI, neural networks drive automatic speech recognition (ASR), text-to-speech synthesis, speaker identification, and natural language understanding. End-to-end neural network models can convert raw audio directly into text, reducing the need for hand-engineered feature extraction and delivering higher accuracy across diverse accents and languages.