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Low-Resource Speech Recognition and Keyword-Spotting

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Speech and Computer (SPECOM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10458))

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Abstract

The IARPA Babel program ran from March 2012 to November 2016. The aim of the program was to develop agile and robust speech technology that can be rapidly applied to any human language in order to provide effective search capability on large quantities of real world data. This paper will describe some of the developments in speech recognition and keyword-spotting during the lifetime of the project. Two technical areas will be briefly discussed with a focus on techniques developed at Cambridge University: the application of deep learning for low-resource speech recognition; and efficient approaches for keyword spotting. Finally a brief analysis of the Babel speech language characteristics and language performance will be presented.

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Notes

  1. 1.

    For a complete movie of the activation functions for stimulated training see: http://mi.eng.cam.ac.uk/~mjfg/bneStimu.avi.

  2. 2.

    All markers such as accents are stripped from the grapheme to yield the root grapheme. Thus Latin scripts have 26 graphemes. These accuracies include silence at the beginning and end of sentences, and between all words.

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Acknowledgements

This work was supported in part by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Defense U.S. Army Research Laboratory (DoD/ARL) contract number W911NF-12-C-0012. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoD/ARL, or the U.S. Government. This work made use of data provided by IARPA The following data was used in the FLP configuration:

IARPA-babel106-v0.2f, IARPA-babel202b-v1.0d, IARPA-babel204b-v1.1b, IARPA-babel205b-v1.0a, IARPA-babel206b-v0.1d, IARPA-babel207b-v1.0a, IARPA-babel301b-v1.0b, IARPA-babel302b-v1.0a, IARPA-babel303b-v1.0a, IARPA-babel304b-v1.0b, IARPA-babel104b-v0.4bY, IARPA-babel306b-v2.0c, IARPA- babel401b-v2.0b, IARPA-babel402b-v1.0b, IARPA-babel403b-v1.0b, IARPA-babel404b-v1.0a, ...

The authors would like to thank the contributions of all the members of the CUED Babel team during the project. In particular Dr X. Chen, J. Vasilakes, Dr H. Wang and Dr S. Rath who directly worked on the evaluation systems and during the “interesting” Babel evaluation periods. The authors would also like to thank all the members of the LORELEI team, in particular the IBM and RWTH Aachen Babel teams.

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Authors and Affiliations

  1. Engineering Department, Cambridge University, Trumpington Street, Cambridge, UK

    Mark J. F. Gales, Kate M. Knill & Anton Ragni

Authors
  1. Mark J. F. Gales
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  2. Kate M. Knill
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  3. Anton Ragni
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Correspondence to Mark J. F. Gales .

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Editors and Affiliations

  1. SPIIRAS, Saint Petersburg, Russia

    Alexey Karpov

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

  3. University of Hertfordshire, Hatfield, United Kingdom

    Iosif Mporas

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Gales, M.J.F., Knill, K.M., Ragni, A. (2017). Low-Resource Speech Recognition and Keyword-Spotting. In: Karpov, A., Potapova, R., Mporas, I. (eds) Speech and Computer. SPECOM 2017. Lecture Notes in Computer Science(), vol 10458. Springer, Cham. https://doi.org/10.1007/978-3-319-66429-3_1

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  • DOI: https://doi.org/10.1007/978-3-319-66429-3_1

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  • Online ISBN: 978-3-319-66429-3

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