ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages

Raghav Menon; Herman Kamper; Emre Yilmaz; John Quinn; Thomas Niesler

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arXiv · 1807.08666

ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages

Raghav Menon·Herman Kamper·Emre Yilmaz·John Quinn·Thomas Niesler

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Abstract

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.

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Raghav Menon, Herman Kamper, Emre Yilmaz, John Quinn, Thomas Niesler. 2018-07-23. ASR-free CNN-DTW keyword spotting using multilingual bottleneck features for almost zero-resource languages. https://arxiv.org/abs/1807.08666

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