End-to-end Streaming model for Low-Latency Speech Anonymization
<|reference_start|>End-to-end Streaming model for Low-Latency Speech Anonymization: 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.<|reference_end|>
@article{quamer2024end-to-end,
    title={End-to-end streaming model for low-latency speech anonymization},
    author={Waris Quamer and Ricardo Gutierrez-Osuna},
    journal={arXiv preprint arXiv:2406.09277},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09277},
    primaryClass={eess.AS cs.CL cs.LG}
}