  Speaker anonymization aims to conceal cues to speaker identity while
preserving linguistic content. Current machine learning based approaches
require substantial computational resources, hindering real-time streaming
applications. To address these concerns, we propose a streaming model that
achieves speaker anonymization with low latency. The system is trained in an
end-to-end autoencoder fashion using a lightweight content encoder that
extracts HuBERT-like information, a pretrained speaker encoder that extract
speaker identity, and a variance encoder that injects pitch and energy
information. These three disentangled representations are fed to a decoder that
resynthesizes the speech signal. We present evaluation results from two
implementations of our system, a full model that achieves a latency of 230ms,
and a lite version (0.1x in size) that further reduces latency to 66ms while
maintaining state-of-the-art performance in naturalness, intelligibility, and
privacy preservation.
