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MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
We present an MatchboxNet - an end-to-end neural network for speech command recognition. MatchboxNet is a deep residual network composed from blocks of 1D time-channel separable convolution, batch-normalization, ReLU and dropout layers. MatchboxNet reaches state-of-the-art accuracy on the Google Speech Commands dataset while having significantly fewer parameters than similar models. The small footprint of MatchboxNet makes it an attractive candidate for devices with limited computational resources. The model is highly scalable, so model accuracy can be improved with modest additional memory and compute. Finally, we show how intensive data augmentation using an auxiliary noise dataset improves robustness in the presence of background noise.
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Code
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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 | MatchboxNet-3x2x64 | Google Speech Commands V1 12 | 97.48 | #6 of 42 | Archive leaderboard | report |
| Keyword Spotting | Google Speech Commands | MatchboxNet-3x2x64 | Google Speech Commands V2 12 | 97.63 | #6 of 42 | Archive leaderboard | report |
| Time Series Analysis | Speech Commands | MatchboxNet | % Test Accuracy | 97.40 | #4 of 6 | Archive leaderboard | report |
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