In this paper, we investigate representation learning for low-resource
keyword spotting (KWS). The main challenges of KWS are limited labeled data and
limited available device resources. To address those challenges, we explore
representation learning for KWS by self-supervised contrastive learning and
self-training with pretrained model. First, local-global contrastive siamese
networks (LGCSiam) are designed to learn similar utterance-level
representations for similar audio samplers by proposed local-global contrastive
loss without requiring ground-truth. Second, a self-supervised pretrained
Wav2Vec 2.0 model is applied as a constraint module (WVC) to force the KWS
model to learn frame-level acoustic representations. By the LGCSiam and WVC
modules, the proposed small-footprint KWS model can be pretrained with
unlabeled data. Experiments on speech commands dataset show that the
self-training WVC module and the self-supervised LGCSiam module significantly
improve accuracy, especially in the case of training on a small labeled
dataset.

本文探讨了如何利用自监督对比学习和预训练模型来进行低资源关键词检测中的表征学习，通过构建局部 - 全局对比同构网络和利用预训练 Wav2Vec 2.0 模型，该小型模型可以利用未标记的数据进行预训练，实验结果表明在小的标记数据集的情况下，自我训练的 WVC 模块和自监督的 LGCSiam 模块可以显著提高准确性。

探索用于小尺寸关键词检测的表示学习

Exploring Representation Learning for Small-Footprint Keyword Spotting

We consider multilingual bottleneck features (BNFs) for nearly zero-resource
keyword spotting. This forms part of a United Nations effort using keyword
spotting to support humanitarian relief programmes in parts of Africa where
languages are severely under-resourced. We use 1920 isolated keywords (40
types, 34 minutes) as exemplars for dynamic time warping (DTW) template
matching, which is performed on a much larger body of untranscribed speech.
These DTW costs are used as targets for a convolutional neural network (CNN)
keyword spotter, giving a much faster system than direct DTW. Here we consider
how available data from well-resourced languages can improve this CNN-DTW
approach. We show that multilingual BNFs trained on ten languages improve the
area under the ROC curve of a CNN-DTW system by 10.9% absolute relative to the
MFCC baseline. By combining low-resource DTW-based supervision with information
from well-resourced languages, CNN-DTW is a competitive option for low-resource
keyword spotting.

本文介绍一种基于多语言瓶颈特征（BNFs）的零资源关键字识别方法，将短语音片段进行动态时间规整（DTW）对齐，并使用卷积神经网络（CNN）进行关键字识别，同时借鉴多种资源语言，提高 DTW-CNN 方法的准确性，为低资源语言关键字识别提供了一种有竞争力的选择。