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Adaptation and Training of a Swiss German Speech Recognition System using Data-driven Pronunciation Modelling

 
: Stadtschnitzer, Michael; Schmidt, Christoph Andreas

:
Preprint urn:nbn:de:0011-n-5033592 (174 KByte PDF)
MD5 Fingerprint: bbfd0ed964017329b7e407522b128bc2
Created on: 15.04.2019


Seeber, B. ; Deutsche Gesellschaft für Akustik -DEGA-, Berlin:
Fortschritte der Akustik. DAGA 2018 : 44. Jahrestagung für Akustik, 19.-22. März 2018, München
Berlin: DEGA, 2018
ISBN: 978-3-939296-13-3
ISBN: 3-939296-13-9
pp.541-542
Deutsche Jahrestagung für Akustik (DAGA) <44, 2018, München>
English
Conference Paper, Electronic Publication
Fraunhofer IAIS ()

Abstract
Automatic speech recognition is a very important technique for numerous applications like automatic subtitling, dialogue systems and information retrieval systems. Given an annotated speech corpus, a phonetic lexicon and a text corpus, the training of speech recognition systems is straight forward. However, sometimes some of these resources are not available, and strategies must be explored to fill this gap. In this work we train a Swiss German speech recognition system. The only resources that are available is a small Swiss German speech corpus, which is annotated with standard German text. Standard German is the desired output of the speech recognition system, since there is no standardized way to write Swiss German. We use a data-driven approach to estimate the Swiss German pronunciatio ns from a standard German speech recognition model, to improve the Swiss German speech recognition system. Evaluations of the Swiss German speech recognition system show promising results.

: http://publica.fraunhofer.de/documents/N-503359.html