  In this paper, we discuss an important aspect of speech privacy: protecting
spoken content. New capabilities from the field of machine learning provide a
unique and timely opportunity to revisit speech content protection. There are
many different applications of content privacy, even though this area has been
under-explored in speech technology research. This paper presents several
scenarios that indicate a need for speech content privacy even as the specific
techniques to achieve content privacy may necessarily vary. Our discussion
includes several different types of content privacy including recoverable and
non-recoverable content. Finally, we introduce evaluation strategies as well as
describe some of the difficulties that may be encountered.
