NWPU-ASLP System for the VoicePrivacy 2022 Challenge

TL;DR

This paper introduces a speaker anonymization system for the VoicePrivacy 2022 Challenge that generates anonymized speech without relying on external speaker verification models, using a modular neural approach.

Contribution

The system uniquely combines feature extraction, pseudo speaker embedding generation, and neural vocoding without additional ASV models, advancing speaker anonymization techniques.

Findings

Effective anonymization demonstrated in experiments

No reliance on external speaker verification models

Generates natural-sounding anonymized speech

Abstract

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.