  Over the recent years, various deep learning-based embedding methods have
been proposed and have shown impressive performance in speaker verification.
However, as in most of the classical embedding techniques, the deep
learning-based methods are known to suffer from severe performance degradation
when dealing with speech samples with different conditions (e.g., recording
devices, emotional states). In this paper, we propose a novel fully supervised
training method for extracting a speaker embedding vector disentangled from the
variability caused by the nuisance attributes. The proposed framework was
compared with the conventional deep learning-based embedding methods using the
RSR2015 and VoxCeleb1 dataset. Experimental results show that the proposed
approach can extract speaker embeddings robust to channel and emotional
variability.
