  The increasing availability of audio data on the internet lead to a multitude
of datasets for development and training of text to speech applications, based
on neural networks. Highly differing quality of voice, low sampling rates, lack
of text normalization and disadvantageous alignment of audio samples to
corresponding transcript sentences still limit the performance of deep neural
networks trained on this task. Additionally, data resources in languages like
German are still very limited. We introduce the "HUI-Audio-Corpus-German", a
large, open-source dataset for TTS engines, created with a processing pipeline,
which produces high quality audio to transcription alignments and decreases
manual effort needed for creation.
