Papers › ImportantAug: a data augmentation agent for speech
ImportantAug: a data augmentation agent for speech
Viet Anh Trinh, Hassan Salami Kavaki, Michael I Mandel
We introduce ImportantAug, a technique to augment training data for speech classification and recognition models by adding noise to unimportant regions of the speech and not to important regions. Importance is predicted for each utterance by a data augmentation agent that is trained to maximize the amount of noise it adds while minimizing its impact on recognition performance. The effectiveness of our method is illustrated on version two of the Google Speech Commands (GSC) dataset. On the standard GSC test set, it achieves a 23.3% relative error rate reduction compared to conventional noise augmentation which applies noise to speech without regard to where it might be most effective. It also provides a 25.4% error rate reduction compared to a baseline without data augmentation. Additionally, the proposed ImportantAug outperforms the conventional noise augmentation and the baseline on two test sets with additional noise added.
Code
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Keyword Spotting | Google Speech Commands | ImportantAug | Google Speech Command-Musan | 86.7 | #35 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | ImportantAug | Google Speech Commands V2 35 | 95 | #35 of 42 | Archive leaderboard | report |
| Speech Recognition | Google Speech Commands - Musan | ImportantAug | Error rate - SNR 0dB | 13.3 | #1 of 1 | 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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