Papers › A neural attention model for speech command recognition
A neural attention model for speech command recognition
Douglas Coimbra de Andrade, Sabato Leo, Martin Loesener Da Silva Viana, Christoph Bernkopf
This paper introduces a convolutional recurrent network with attention for speech command recognition. Attention models are powerful tools to improve performance on natural language, image captioning and speech tasks. The proposed model establishes a new state-of-the-art accuracy of 94.1% on Google Speech Commands dataset V1 and 94.5% on V2 (for the 20-commands recognition task), while still keeping a small footprint of only 202K trainable parameters. Results are compared with previous convolutional implementations on 5 different tasks (20 commands recognition (V1 and V2), 12 commands recognition (V1), 35 word recognition (V1) and left-right (V1)). We show detailed performance results and demonstrate that the proposed attention mechanism not only improves performance but also allows inspecting what regions of the audio were taken into consideration by the network when outputting a given category.
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Code
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Code Syntology ran Syntology
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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 | Attention RNN | Google Speech Commands V1 12 | 95.6 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V1 2 | 99.2 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V1 20 | 94.1 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V1 35 | 94.3 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V2 12 | 96.9 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V2 2 | 99.4 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V2 20 | 94.5 | #13 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | Attention RNN | Google Speech Commands V2 35 | 93.9 | #13 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.
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