We propose a max-pooling based loss function for training Long Short-Term Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low CPU, memory, and latency requirements. The max-pooling loss training can be further guided by initializing with a cross-entropy loss trained network. A posterior smoothing based evaluation approach is employed to measure keyword spotting performance. Our experimental results show that LSTM models trained using cross-entropy loss or max-pooling loss outperform a cross-entropy loss trained baseline feed-forward Deep Neural Network (DNN). In addition, max-pooling loss trained LSTM with randomly initialized network performs better compared to cross-entropy loss trained LSTM. Finally, the max-pooling loss trained LSTM initialized with a cross-entropy pre-trained network shows the best performance, which yields $67.6\%$ relative reduction compared to baseline feed-forward DNN in Area Under the Curve (AUC) measure.

提出了一种基于最大池化的损失函数来训练CPU、内存和延迟需求较低的小型基于关键词识别（KWS）的长短时记忆网络（LSTM）模型。研究结果表明，与交叉熵损失训练的前馈深度神经网络相比，通过使用交叉熵或最大池化损失训练的LSTM模型的性能更好。此外，最大池化损失训练的LSTM模型表现也优于交叉熵损失训练的LSTM模型，初始化为交叉熵预训练网络的最大池化损失训练的LSTM则表现最佳，其在面积下的曲线（AUC）测量方面的性能相对于前馈深度神经网络减少了67.6％。

用最大池化损失训练长短时记忆网络实现小尺寸关键词定位