Keyword Spotting (KWS) models on embedded devices should adapt fast to new
user-defined words without forgetting previous ones. Embedded devices have
limited storage and computational resources, thus, they cannot save samples or
update large models. We consider the setup of embedded online continual
learning (EOCL), where KWS models with frozen backbone are trained to
incrementally recognize new words from a non-repeated stream of samples, seen
one at a time. To this end, we propose Temporal Aware Pooling (TAP) which
constructs an enriched feature space computing high-order moments of speech
features extracted by a pre-trained backbone. Our method, TAP-SLDA, updates a
Gaussian model for each class on the enriched feature space to effectively use
audio representations. In experimental analyses, TAP-SLDA outperforms
competitors on several setups, backbones, and baselines, bringing a relative
average gain of 11.3% on the GSC dataset.

嵌入式设备上的关键词检测模型需要快速适应新用户定义的词汇，同时不会遗忘之前的词汇。为此，我们提出了一种新的方法 ——Temporal Aware Pooling (TAP)，用于构建丰富的特征空间，通过计算预先训练主干架构提取的语音特征的高阶矩。我们的方法，TAP-SLDA，在实验分析中在多个设置、主干架构和基准测试中表现优异，在 GSC 数据集上相对平均增益达到 11.3%。