Decaf: A privacy preserving speech codec using speaker disentanglement and canonical voice conversion

Abstract

We present DECAF, a privacy preserving neural speech codec that obfuscates a speaker's voice while preserving linguistic content while maintaining automatic speech recognition (ASR) performance at very low bitrates, inspired by decaffeination. At the transmitter end, speech is encoded into speaker independent content embeddings, which are compressed using residual vector quantization and transmitted without any speaker related information. At the receiver, a canonical speaker embedding, shared a priori between endpoints, is used for waveform reconstruction, enabling deterministic and consistent obfuscation of a speaker's voice. The proposed framework leverages an information bottleneck applied to self supervised representations, along with a separate speaker embedding branch, to achieve effective speaker content disentanglement. We further incorporate a CTC-based auxiliary objective, encouraging content representations that are well aligned with downstream ASR tasks. We show that DECAF operating at a bit rate of 0.5 kbps achieves an Equal Error Rate (EER) of up to 43.5% for a speaker verification system, while maintaining competitive ASR performance, yielding a relative reduction in word error rate of 33.2% compared to a state of the art method.