Language detection is a key part of the NLP pipeline for text processing. The task of automatically detecting languages belonging to disjoint groups is relatively easy. It is considerably challenging to detect languages that have similar origins or dialects. This paper describes Idiap’s submission to the 2020 Germeval evaluation campaign1on Swiss-German language detection. In this work, we have given high dimensional features generated from the text data as input to a supervised autoencoder for detecting languages with dialect variances. Bayesian optimizer was used to fine-tune the hyper-parameters of the supervised autoencoder. To the best of our knowledge,we are first to apply supervised autoen-coder for the language detection task