PROBING THE INFORMATION ENCODED IN X-VECTORS
Table 3: Performance of the embeddings in predicting the 10 common phrase utterances. P, R, and F denote precision, recall, and F-score, respectively.
['[BOLD] Dim', '[BOLD] i-vector  [BOLD] P', '[BOLD] i-vector  [BOLD] R', '[BOLD] i-vector  [BOLD] F', '[BOLD] x-vector  [BOLD] P', '[BOLD] x-vector  [BOLD] R', '[BOLD] x-vector  [BOLD] F']
Since several of these utterances are spoken by multiple speakers, it seems very plausible that to achieve a high recognition performance, the embedding must incidentally capture some transcription information in addition to the speaker information. Although the x-vector DNN is trained on a large amount of unconstrained speech to predict only the speaker label, we find the x-vectors (with or without augmentation) are still not wholly invariant to lexical content, and they do retain information about what was spoken. It may be observed that x-vectors are good at recognizing words such as google, lawyers, password, and so on, but fare poorly on more common words such as and, he, and in. While i-vectors show a similar trend, the difference is less pronounced.