Multitask Learning for Grapheme-to-Phoneme Conversion of Anglicisms in German Speech Recognition
This work addresses the challenge of Anglicism pronunciation in German speech recognition, which is an incremental improvement for this specific domain.
The paper tackled the problem of faulty phoneme sequences for Anglicisms in German speech recognition by proposing a multitask sequence-to-sequence approach for grapheme-to-phoneme conversion, resulting in a reduction of word error rate by 1% and Anglicism error rate by 3% on a dedicated evaluation set.
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.