We use dynamic time warping (DTW) as supervision for training a convolutional neural network (CNN) based keyword spotting system using a small set of spoken isolated keywords. The aim is to allow rapid deployment of a keyword spotting system in a new language to support urgent United Nations (UN) relief programmes in parts of Africa where languages are extremely under-resourced and the development of annotated speech resources is infeasible. First, we use 1920 recorded keywords (40 keyword types, 34 minutes of speech) as exemplars in a DTW-based template matching system and apply it to untranscribed broadcast speech. Then, we use the resulting DTW scores as targets to train a CNN on the same unlabelled speech. In this way we use just 34 minutes of labelled speech, but leverage a large amount of unlabelled data for training. While the resulting CNN keyword spotter cannot match the performance of the DTW-based system, it substantially outperforms a CNN classifier trained only on the keywords, improving the area under the ROC curve from 0.54 to 0.64. Because our CNN system is several orders of magnitude faster at runtime than the DTW system, it represents the most viable keyword spotter on this extremely limited dataset.

本研究使用动态时间规整作为卷积神经网络关键词识别系统的训练监督技术，旨在利用少量带注释的语音语料，并结合大量未标记数据进行训练，从而在 UN 急需援助的非洲地区提供语音识别服务。结果表明，使用动态时间规整后的卷积神经网络系统，虽然不能达到动态时间规整系统的表现，但在极度有限的数据集上仍比使用传统卷积神经网络分类器的表现更优。

使用DTW和CNN进行快速ASR免费的几乎零资源关键词检测——人道主义监控