End-to-end streaming model for low-latency speech anonymization

• URL: http://arxiv.org/abs/2406.09277v2
• Date: Thu, 31 Oct 2024 20:45:16 GMT
• Title: End-to-end streaming model for low-latency speech anonymization
• Authors: Waris Quamer, Ricardo Gutierrez-Osuna,
• Abstract summary: 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. We present evaluation results from two implementations of our system.
• Score: 11.098498920630782
• License:
• Abstract: 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 re-synthesizes 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.

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