# Privacy-preserving Prosody Representation Learning Source: [Privacy-preserving Prosody Representation Learning]() Collection index: [llms.txt]() Data updated: 2026-06-02T05:00:51.557Z Authors: Kevin Everson, Mari Ostendorf Published: 2026-05-29T22:49:56.000Z ## Abstract Speech representations that capture prosodic information can be useful for both understanding and generation. However, speaker characteristics are reflected in acoustic-prosodic features (e.g., pitch). To address privacy concerns from the leakage of identity information, we propose a new self-supervised approach to learning prosody representations that incorporates speaker disentanglement strategies. We evaluate our encoder on three tasks to probe representation capabilities, including pitch reconstruction and detection of different prosodic events. Our encoder outperforms raw prosody and HuBERT-base baselines, achieving strong speaker disentanglement without adverse impact on prosody-related downstream tasks. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Phrase boundary detection on ToBI (test)](): Phrase boundary detection; ToBI (test); F1 Score: 82. - [Syllable prominence detection on ToBI (test)](): Syllable prominence detection; ToBI (test); F1 Score: 86. - [Pitch Reconstruction on LibriTTS (test)](): Pitch reconstruction; LibriTTS (test); Standard MSE: 0.024. ## Original sources and implementation [Original paper]() [PDF]()