  Most recent speech privacy efforts have focused on anonymizing acoustic
speaker attributes but there has not been as much research into protecting
information from speech content. We introduce a toy problem that explores an
emerging type of privacy called "content masking" which conceals selected words
and phrases in speech. In our efforts to define this problem space, we evaluate
an introductory baseline masking technique based on modifying sequences of
discrete phone representations (phone codes) produced from a pre-trained
vector-quantized variational autoencoder (VQ-VAE) and re-synthesized using
WaveRNN. We investigate three different masking locations and three types of
masking strategies: noise substitution, word deletion, and phone sequence
reversal. Our work attempts to characterize how masking affects two downstream
tasks: automatic speech recognition (ASR) and automatic speaker verification
(ASV). We observe how the different masks types and locations impact these
downstream tasks and discuss how these issues may influence privacy goals.
