Multilingual BLSTM and speaker-specific vector adaptation in 2016 but babel system

Abstract

This paper provides an extensive summary of BUT 2016 system for the last IARPA Babel evaluations. It concentrates on multi-lingual training of both deep neural network (DNN)-based feature extraction and acoustic models including multilingual training of bidirectional Long Short Term memory networks. Next, two low-dimensional vector approaches to speaker adaptation are investigated: i-vectors and sequence-summarizing neural networks (SSNN). The results provided on three Babel Year 4 languages show clear advantage of both approaches in case limited amount of training data is available. The time necessary for the development of a new system is addressed too, as some of the investigated techniques do not require extensive re-training of the whole system.