conformer-convolution-augmented-transformer-speech
IN premise — summaries/2026/08/24/wiki-Transformer_deep_learning_architecture-chunk-6-chunk-1.md
Created 2026-08-24T17:11:26+00:00
Conformer (Gulati et al., 2020) is a convolution-augmented Transformer architecture for speech recognition that injects local convolution into the Transformer block and was deployed in Google Speech.
Summary
The Conformer is a speech-recognition design that stitches short-range convolution directly into a Transformer block, letting the model capture both fine acoustic detail and broad contextual patterns in one architecture. Its production deployment in Google's speech system proves the hybrid approach works at scale, making it a validated reference point rather than just a paper design.