Learning To Detect Keyword Parts And Whole By Smoothed Max Pooling

基于谱-时间图注意力池化与多任务学习的示例查询关键词识别

Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning

本文提出一种基于谱-时间图注意力池化和多任务学习的示例查询关键词识别系统，通过三种编码器架构对比实验证明该框架能使13倍高效的LiCoNet达到与Conformer相当的性能。

面向低资源设备的关键词识别在线持续学习：通过池化高阶时序统计量实现

Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics

针对嵌入式设备关键词识别的在线持续学习需求，提出时序感知池化构建增强特征空间，结合TAP-SLDA方法增量更新类别高斯模型，在GSC数据集上取得11.3%的相对平均性能提升。

使用向量量化的无监督语音表示池化

Unsupervised Speech Representation Pooling Using Vector Quantization

针对语音表示长度可变的池化问题，提出一种无需额外训练的基于向量量化的无监督池化方法，通过压缩声学相似表示，在多个下游任务上优于平均池化。

WeKws: A production first small-footprint end-to-end Keyword Spotting Toolkit

Keyword spotting (KWS) enables speech-based user interaction and gradually becomes an indispensable component of smart devices. Recently, end-to-end (E2E) methods have become the most popular approach for on-device KWS tasks. However, there is still a gap between the research and deployment of E2E KWS methods. In this paper, we introduce WeKws, a production-quality, easy-to-build, and convenient-to-be-applied E2E KWS toolkit. WeKws contains the implementations of several state-of-the-art backbone networks, making it achieve highly competitive results on three publicly available datasets. To ma…

Efficient keyword spotting using dilated convolutions and gating

We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states. We propose a model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in resource-constrained configurations. Gated activations and residual connections are also added, following a similar configuration to WaveNet. In addition, we apply a custom target labeling that back-propagates loss from specific frames of intere…

Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting

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 additio…