The ground truth used for training image, video, or speech quality prediction models is based on the Mean Opinion Scores (MOS) obtained from subjective experiments.
In this paper, we present an update to the NISQA speech quality prediction model that is focused on distortions that occur in communication networks.
The quality of the speech communication systems, which include noise suppression algorithms, are typically evaluated in laboratory experiments according to the ITU-T Rec.
The subjective quality of transmitted speech is traditionally assessed in a controlled laboratory environment according to ITU-T Rec.
This paper presents TextComplexityDE, a dataset consisting of 1000 sentences in German language taken from 23 Wikipedia articles in 3 different article-genres to be used for developing text-complexity predictor models and automatic text simplification in German language.