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。