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2017 | OriginalPaper | Buchkapitel

Low-Resource Speech Recognition and Keyword-Spotting

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

Erschienen in: Speech and Computer

Verlag: 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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Nächstes Kapitel Big Data, Deep Learning – At the Edge of X-Ray Speaker Analysis
Fußnoten
1
For a complete movie of the activation functions for stimulated training see: http://​mi.​eng.​cam.​ac.​uk/​~mjfg/​bneStimu.​avi.
 
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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Metadaten
Titel
Low-Resource Speech Recognition and Keyword-Spotting
verfasst von
Mark J. F. Gales
Kate M. Knill
Anton Ragni
Copyright-Jahr
2017
Buch
Speech and Computer

Print ISBN: 978-3-319-66428-6

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

Copyright-Jahr: 2017

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