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2017 | OriginalPaper | Chapter

Low-Resource Speech Recognition and Keyword-Spotting

Authors : Mark J. F. Gales, Kate M. Knill, Anton Ragni

Published in: Speech and Computer

Publisher: Springer International Publishing

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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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next chapter Big Data, Deep Learning – At the Edge of X-Ray Speaker Analysis

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Title: Low-Resource Speech Recognition and Keyword-Spotting
Authors: Mark J. F. Gales
Kate M. Knill
Anton Ragni
Publisher: Springer International Publishing
Book: Speech and Computer
Print ISBN: 978-3-319-66428-6

Electronic ISBN: 978-3-319-66429-3

Copyright Year: 2017
DOI: https://doi.org/10.1007/978-3-319-66429-3_1

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