  Deep neural network based speaker embeddings, such as x-vectors, have been
shown to perform well in text-independent speaker recognition/verification
tasks. In this paper, we use simple classifiers to investigate the contents
encoded by x-vector embeddings. We probe these embeddings for information
related to the speaker, channel, transcription (sentence, words, phones), and
meta information about the utterance (duration and augmentation type), and
compare these with the information encoded by i-vectors across a varying number
of dimensions. We also study the effect of data augmentation during extractor
training on the information captured by x-vectors. Experiments on the RedDots
data set show that x-vectors capture spoken content and channel-related
information, while performing well on speaker verification tasks.
