MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition
Abstract
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.
中文速览
语音指令识别（speech command recognition）长期面临一个矛盾：模型要足够准确，又要足够小巧才能跑在手机、智能音箱等算力受限的设备上。MatchboxNet 用一种由"一维时间-通道可分离卷积（1D time-channel separable convolution）"堆叠而成的深度残差网络来破解这个矛盾——相比普通卷积，这种结构能大幅压缩参数量，同时保留强大的特征提取能力。在 Google Speech Commands 数据集上，MatchboxNet 以远少于同类模型的参数量达到了当时最优的识别准确率，并且通过调整网络深度或通道数就能灵活扩展，适配不同硬件规格。此外，研究者还用从 Freesound 数据库收集的大量环境噪声和背景人声对模型进行增强训练，使其在低信噪比（SNR 低至 -10 dB）条件下仍能保持良好鲁棒性，为在噪声环境中实际部署关键词检测（keyword spotting）系统提供了切实可行的方案。
原文 arXiv:2004.08531;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2004.08531v2