The 2016 RWTH Keyword Search System for Low-Resource Languages

  • Pavel Golik
  • Zoltán Tüske
  • Kazuki Irie
  • Eugen Beck
  • Ralf Schlüter
  • Hermann Ney
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10458)

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.

Keywords

Acoustic modeling Keyword search Graphemic Multilingual Neural networks Time-marked word list Recurrent LSTM 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Pavel Golik
    • 1
  • Zoltán Tüske
    • 1
  • Kazuki Irie
    • 1
  • Eugen Beck
    • 1
  • Ralf Schlüter
    • 1
  • Hermann Ney
    • 1
  1. 1.Human Language Technology and Pattern Recognition, Computer Science DepartmentRWTH Aachen UniversityAachenGermany

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