The RWTH Aachen German and English LVCSR systems for IWSLT-2013

Abstract In this paper, German and English large vocabulary continuous speech recognition (LVCSR) systems developed by the RWTH Aachen University for the IWSLT-2013 evaluation campaign are presented. Good improvements are obtained with state-of-the-art monolingual and multilingual bottleneck features. In addition, an open vocabulary approach using morphemic sub-lexical units is investigated along with the language model adaptation for the German LVCSR. For both the languages, competitive WERs are achieved using system combination

Improving bottleneck features for Vietnamese large vocabulary continuous speech recognition system using deep neural networks

In this paper, the pre-training method based on denoising auto-encoder is investigated and proved to be good models for initializing bottleneck networks of Vietnamese speech recognition system that result in better recognition performance compared to base bottleneck features reported previously. The experiments are carried out on the dataset containing speeches on Voice of Vietnam channel (VOV). The results show that the DBNF extraction for Vietnamese recognition decreases relative word error rate by 14 % and 39 % compared to the base bottleneck features and MFCC baseline, respectively

Multilingual representations for low resource speech recognition and keyword search

© 2015 IEEE. This paper examines the impact of multilingual (ML) acoustic representations on Automatic Speech Recognition (ASR) and keyword search (KWS) for low resource languages in the context of the OpenKWS15 evaluation of the IARPA Babel program. The task is to develop Swahili ASR and KWS systems within two weeks using as little as 3 hours of transcribed data. Multilingual acoustic representations proved to be crucial for building these systems under strict time constraints. The paper discusses several key insights on how these representations are derived and used. First, we present a data sampling strategy that can speed up the training of multilingual representations without appreciable loss in ASR performance. Second, we show that fusion of diverse multilingual representations developed at different LORELEI sites yields substantial ASR and KWS gains. Speaker adaptation and data augmentation of these representations improves both ASR and KWS performance (up to 8.7% relative). Third, incorporating un-transcribed data through semi-supervised learning, improves WER and KWS performance. Finally, we show that these multilingual representations significantly improve ASR and KWS performance (relative 9% for WER and 5% for MTWV) even when forty hours of transcribed audio in the target language is available. Multilingual representations significantly contributed to the LORELEI KWS systems winning the OpenKWS15 evaluation

DEEP HIERARCHICAL BOTTLENECK MRASTA FEATURES FOR LVCSR

Hierarchical Multi Layer Perceptron (MLP) based long-term feature extraction is optimized for TANDEM connectionist large vocabulary continuous speech recognition (LVCSR) system within the QUAERO project. Training the bottleneck MLP on multiresolutional RASTA filtered critical band energies, more than 20% relative word error rate (WER) reduction over standard MFCC system is observed after optimizing the number of target labels. Furthermore, introducing a deeper structure in the hierarchical bottleneck processing the relative gain increases to 25%. The final system based on deep bottleneck TANDEM features clearly outperforms the hybrid approach, even if the long-term features are also presented to the deep MLP acoustic model. The results are also verified on evaluation data of the year 2012, and about 20 % relative WER improvement over classical cepstral system is measured even after speaker adaptive training