Low-resource speech recognition and keyword-spotting
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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Gales, M., Knill, K., & Ragni, A. (2017). Low-resource speech recognition and keyword-spotting. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10458 LNAI 3-19. https://doi.org/10.1007/978-3-319-66429-3_1
© Springer International Publishing AG 2017. 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.
External DOI: https://doi.org/10.1007/978-3-319-66429-3_1
This record's URL: https://www.repository.cam.ac.uk/handle/1810/274282