Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition

Anglicisms are a challenge in German speech recognition. Due to their irregular pronunciation compared to native German words, automatically generated pronunciation dictionaries often include faulty phoneme sequences for Anglicisms. In this work, we propose a multitask sequence-to-sequence approach...

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Bibliographic Details
Published inarXiv.org
Main Authors Pritzen, Julia, Gref, Michael, Zühlke, Dietlind, Schmidt, Christoph
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.01.2022
Subjects
Computer Science - Computation and Language
Computer Science - Sound
Conversion
Dictionaries
German language
Learning
Model testing
Phonemes
Speech recognition
Voice recognition
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Summary:Anglicisms are a challenge in German speech recognition. Due to their irregular pronunciation compared to native German words, automatically generated pronunciation dictionaries often include faulty phoneme sequences for Anglicisms. In this work, we propose a multitask sequence-to-sequence approach for grapheme-to-phoneme conversion to improve the phonetization of Anglicisms. We extended a grapheme-to-phoneme model with a classifier to distinguish Anglicisms from native German words. With this approach, the model learns to generate pronunciations differently depending on the classification result. We used our model to create supplementary Anglicism pronunciation dictionaries that are added to an existing German speech recognition model. Tested on a dedicated Anglicism evaluation set, we improved the recognition of Anglicisms compared to a baseline model, reducing the word error rate by 1 % and the Anglicism error rate by 3 %. We show that multitask learning can help solving the challenge of Anglicisms in German speech recognition.
ISSN:2331-8422
DOI:10.48550/arxiv.2105.12708