MatchboxNet：用于语音命令识别的一维时间通道可分离卷积神经网络结构
MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
摘要：
我们提出了一个MatchboxNet -一个用于语音命令识别的端到端神经网络。MatchboxNet是一个深度残差网络，由1D时间通道可分离卷积、批量归一化、ReLU和dropout层组成。MatchboxNet在Google Speech Commands数据集上达到了最先进的准确性，同时参数比类似模型少得多。MatchboxNet的小尺寸使其成为计算资源有限的设备的有吸引力的候选者。该模型具有高度可扩展性，因此可以通过适度的额外内存和计算来提高模型精度。最后，我们展示了使用辅助噪声数据集的密集数据增强如何在存在背景噪声的情况下提高鲁棒性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
