The 2016 rwth keyword search system for low-resource languages

Abstract

In this paper we describe the RWTH Aachen keyword search (KWS) system developed in the course of the IARPA Babel program. We put focus on acoustic modeling with neural networks and evaluate the full pipeline with respect to the KWS performance. At the core of this study lie multilingual bottleneck features extracted from a deep neural network trained on all 28 languages available to the project articipants. We show that in a low-resource scenario, the multilingual features are crucial for achieving state-of-the-art performance. Further highlights of this work include comparisons of tandem and hybrid acoustic models based on feed-forward and recurrent neural networks, keyword search pipelines based on lattice and time-marked word list representation and measuring the effect of adding large amounts of text data scraped from the web. The evaluation is performed on multiple languages of the last two project periods.