Distance metric learning for posteriorgram based keyword search

In this paper, we propose a neural network based distance metric learning method for a better discrimination in the sequence-matching based keyword search (KWS). In this technique, we conduct a version of Dynamic Time Warping (DTW) based similarity search on the speaker independent posteriorgram space. With this, we aim to compensate for the scarcity of the resources and overcome the out-of-vocabulary (OOV) term problem, which is one of the main issues for KWS on low-resource languages. This distance measure is then used in the DTW-based similarity search, as an alternative and in comparison to the widely and generally used distance metrics. The experiments ran on IARPA Babel Program's Turkish search data show that, the proposed system outperforms the baseline by 6.3% and when combined with the baseline system, the improvement reaches 44.9%.