  Voice anonymisation can be used to help protect speaker privacy when speech
data is shared with untrusted others. In most practical applications, while the
voice identity should be sanitised, other attributes such as the spoken content
should be preserved. There is always a trade-off; all approaches reported thus
far sacrifice spoken content for anonymisation performance. We report what is,
to the best of our knowledge, the first attempt to actively preserve spoken
content in voice anonymisation. We show how the output of an auxiliary
automatic speech recognition model can be used to condition the vocoder module
of an anonymisation system using a set of learnable embedding dictionaries in
order to preserve spoken content. Relative to a baseline approach, and for only
a modest cost in anonymisation performance, the technique is successful in
decreasing the word error rate computed from anonymised utterances by almost
60%.
