Automatic speech recognition for under-resourced languages: A survey
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Besacier, Laurent
Barnard, Etienne
Karpov, Alexey
Schultz, Tanja
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Elsevier
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Abstract
Speech processing for under-resourced languages is an active field of research, which has experienced significant progress during the
past decade. We propose, in this paper, a survey that focuses on automatic speech recognition (ASR) for these languages. The definition
of under-resourced languages and the challenges associated to them are first defined. The main part of the paper is a literature review of
the recent (last 8 years) contributions made in ASR for under-resourced languages. Examples of past projects and future trends when
dealing with under-resourced languages are also presented. We believe that this paper will be a good starting point for anyone interested
to initiate research in (or operational development of) ASR for one or several under-resourced languages. It should be clear, however,
that many of the issues and approaches presented here, apply to speech technology in general (text-to-speech synthesis for instance).
2013 Published by Elsevier B.V.
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L. Besacier, E. Barnard, A. Karpov and T. Schultz, “Automatic speech recognition for under-resourced languages: A survey”, Speech Communications, Vol 56, pp 85-100, 2014. [http://engineering.nwu.ac.za/multilingual-speech-technologies-must/publications]
