DarkStream: real-time speech anonymization with low latency

• URL: http://arxiv.org/abs/2509.04667v1
• Date: Thu, 04 Sep 2025 21:30:25 GMT
• Title: DarkStream: real-time speech anonymization with low latency
• Authors: Waris Quamer, Ricardo Gutierrez-Osuna,
• Abstract summary: We propose DarkStream, a streaming speech synthesis model for real-time speaker anonymization.<n>DarkStream combines a causal waveform encoder, a short look buffer, and transformer-based contextual layers.<n>DarkStream anonymizes speaker identity by injecting a GAN-generated pseudo-speaker embedding into linguistic features from the content encoder.
• Score: 5.872253202878362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose DarkStream, a streaming speech synthesis model for real-time speaker anonymization. To improve content encoding under strict latency constraints, DarkStream combines a causal waveform encoder, a short lookahead buffer, and transformer-based contextual layers. To further reduce inference time, the model generates waveforms directly via a neural vocoder, thus removing intermediate mel-spectrogram conversions. Finally, DarkStream anonymizes speaker identity by injecting a GAN-generated pseudo-speaker embedding into linguistic features from the content encoder. Evaluations show our model achieves strong anonymization, yielding close to 50% speaker verification EER (near-chance performance) on the lazy-informed attack scenario, while maintaining acceptable linguistic intelligibility (WER within 9%). By balancing low-latency, robust privacy, and minimal intelligibility degradation, DarkStream provides a practical solution for privacy-preserving real-time speech communication.

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