  This paper presents the NWPU-ASLP speaker anonymization system for
VoicePrivacy 2022 Challenge. Our submission does not involve additional
Automatic Speaker Verification (ASV) model or x-vector pool. Our system
consists of four modules, including feature extractor, acoustic model,
anonymization module, and neural vocoder. First, the feature extractor extracts
the Phonetic Posteriorgram (PPG) and pitch from the input speech signal. Then,
we reserve a pseudo speaker ID from a speaker look-up table (LUT), which is
subsequently fed into a speaker encoder to generate the pseudo speaker
embedding that is not corresponding to any real speaker. To ensure the pseudo
speaker is distinguishable, we further average the randomly selected speaker
embedding and weighted concatenate it with the pseudo speaker embedding to
generate the anonymized speaker embedding. Finally, the acoustic model outputs
the anonymized mel-spectrogram from the anonymized speaker embedding and a
modified version of HifiGAN transforms the mel-spectrogram into the anonymized
speech waveform. Experimental results demonstrate the effectiveness of our
proposed anonymization system.
