Spoken Term Detection of Zero-Resource Language using Machine Learning

In this paper, we propose a spoken term detection method for detection of terms in zero-resource languages. The proposed method uses the classifier (the speech comparator) trained by a machine learning method combined with the dynamic time warping method. The advantage of the proposed method is that the classifier can be trained using a large language resource that is different from the target language. We exploited the random forest as a classifier, and carried out an experiment of the spoken term detection from Kaqchikel speech. As a result, the proposed method showed better detection performance compared with the method based on the Euclidean distance.