Papers › Keyword Transformer: A Self-Attention Model for Keyword Spotting
Keyword Transformer: A Self-Attention Model for Keyword Spotting
Axel Berg, Mark O'Connor, Miguel Tairum Cruz
The Transformer architecture has been successful across many domains, including natural language processing, computer vision and speech recognition. In keyword spotting, self-attention has primarily been used on top of convolutional or recurrent encoders. We investigate a range of ways to adapt the Transformer architecture to keyword spotting and introduce the Keyword Transformer (KWT), a fully self-attentional architecture that exceeds state-of-the-art performance across multiple tasks without any pre-training or additional data. Surprisingly, this simple architecture outperforms more complex models that mix convolutional, recurrent and attentive layers. KWT can be used as a drop-in replacement for these models, setting two new benchmark records on the Google Speech Commands dataset with 98.6% and 97.7% accuracy on the 12 and 35-command tasks respectively.
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Code
Syntology Ran 1 of 16 code samples harvested from 5 repositories linked to this paper; 15 have no recorded run. Of those that ran: 1 ran · our draft was wrong.
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Code Syntology ran Syntology
16 samples harvested; 1 ran; 0 honoured the contract we drafted; 15 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Keyword Spotting | Google Speech Commands | KWT-3 | Google Speech Commands V1 12 | 97.49 ±0.15 | #5 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-3 | Google Speech Commands V2 12 | 98.56 ±0.07 | #5 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-3 | Google Speech Commands V2 35 | 97.69 ±0.09 | #5 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-2 | Google Speech Commands V1 12 | 97.27 ±0.08 | #8 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-2 | Google Speech Commands V2 12 | 98.43±0.08 | #8 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-2 | Google Speech Commands V2 35 | 97.74 ±0.03 | #8 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-1 | Google Speech Commands V1 12 | 97.26±0.18 | #9 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-1 | Google Speech Commands V2 12 | 98.08±0.10 | #9 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | KWT-1 | Google Speech Commands V2 35 | 96.95±0.14 | #9 of 42 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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