Fact · Speech recognition
By 2012, the performance of hybrid systems, which pair neural networks with HMMs, had surpassed traditional HMM/GMM systems.
Checked against its source on October 17, 2026
In the source's own words
“by 2012, the performance of hybrid systems had surpassed traditional HMM/GMM systems”
Speech and Language Processing, Chapter 16: Automatic Speech Recognition (draft), Daniel Jurafsky and James H. Martin, Stanford University, online manuscript of the third edition, 2026, Chapter 16, history of automatic speech recognition.
Open the original- What it measures
- The textbook credits the change to deep neural networks with many layers, enabled by Moore's law and GPUs, and says that by 2013 for hybrid HMM/MLP feedforward networks, all that mattered was using a lot of data and enough layers.
- What it does not show
- A textbook summary of the field's history. It names the year 2012 for the crossover on the systems it reviews, not on every task.