What Are Transformers in AI?
The transformer is a neural network architecture that replaced earlier sequence-to-sequence models (such as recurrent neural networks) by relying entirely on an attention mechanism to draw global dependencies between input and output. Introduced in the 2017 paper "Attention Is All You Need," transformers have become the dominant architecture in natural language processing, speech recognition, and generative AI.
How Transformers Work
Self-Attention Mechanism
The core innovation is self-attention, which lets the model evaluate the relationship between every pair of tokens in a sequence in parallel. For each token, the model computes three vectors: a query, a key, and a value. Attention scores are derived by comparing queries against keys, then used to produce a weighted sum of values. This allows the model to capture long-range dependencies without processing tokens one at a time.
Encoder-Decoder Structure
The original transformer uses an encoder-decoder layout:
Encoder: Reads the full input sequence and produces contextual representations for each token.
Decoder: Generates output tokens one at a time, attending both to previously generated tokens and to the encoder output.
Many modern systems use only one half. Encoder-only models (like BERT) excel at classification and understanding tasks, while decoder-only models (like GPT) excel at text generation.
Positional Encoding
Because transformers process all tokens in parallel rather than sequentially, they require positional encodings to inject information about token order into the model.
Transformers in Speech Technology
In voice AI, transformers power both automatic speech recognition (ASR) and text-to-speech (TTS) systems. ASR models use transformer encoders to convert audio features into text, while TTS systems use transformer decoders to generate natural-sounding speech from text input. The architecture's ability to model long-range context makes it especially effective for producing coherent, natural prosody in synthesized speech.
Why Transformers Matter
Parallelism: Unlike RNNs, transformers process entire sequences at once, enabling faster training on modern GPUs.
Scalability: Performance improves predictably as model size and training data increase.
Versatility: The same core architecture applies to text, audio, images, and multimodal tasks.