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Joint Standard German and Bavarian Subdialect Identification of Broadcast Speech

: Stadtschnitzer, Michael; Schmidt, Christoph Andreas

Preprint urn:nbn:de:0011-n-5033588 (164 KByte PDF)
MD5 Fingerprint: 56a59c65e2555e86b9767a225ec75d8c
Erstellt am: 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
Deutsche Jahrestagung für Akustik (DAGA) <44, 2018, München>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Automatic speech recognition is a very important technique for numerous applications like automatic subtitling, dialogue systems and information retrieval systems. Speech recognition systems usually perform very well in clean and controlled environments. However they still often fail, when mismatches between the trained models and the testing data are present, e.g. due to noise, reverberation, or dialects. A method to cope with dialects is to identify the dialect in advance, and then use specialized dialectal speech recognition models for the decoding. Also, dialect identification systems have been recently used for targeted advertising, service customization, forensics tasks and for text-to-speech synthesis of regional speech. In this work, we annotate a large quantity of dialectal and st andard German speech from a German broadcaster, and exploit the data to train and evaluate a joint standard German and Bavarian subdialect identification system, that is able to distinguish between standard German and three Bavarian subdialects, namely Bavarian, Swabian and Franconian, with promising performance.