Enforcing Speech Content Privacy in Environmental Sound Recordings using Segment-wise Waveform Reversal
Authors: Modan Tailleur, Mathieu Lagrange, Pierre Aumond, Vincent Tourre
Abstract
Environmental sound recordings often contain intelligible speech, raising privacy concerns that limit analysis, sharing and reuse of data. In this paper, we introduce a method that renders speech unintelligible while preserving both the integrity of the acoustic scene, and the overall audio quality. Our approach involves reversing waveform segments to distort speech content. This process is enhanced through a voice activity detection and speech separation pipeline, which allows for more precise targeting of speech. In order to demonstrate the effectivness of the proposed approach, we consider a three-part evaluation protocol that assesses: 1) speech intelligibility using Word Error Rate (WER), 2) sound sources detectability using Sound source Classification Accuracy-Drop (SCAD) from a widely used pre-trained model, and 3) audio quality using the Fréchet Audio Distance (FAD), computed with our reference dataset that contains unaltered speech. Experiments on this simulated evaluation dataset, which consists of linear mixtures of speech and environmental sound scenes, show that our method achieves satisfactory speech intelligibility reduction (97.9% WER), minimal degradation of the sound sources detectability (2.7% SCAD), and high perceptual quality (FAD of 1.40). An ablation study further highlights the contribution of each component of the pipeline. We also show that incorporating random splicing to our speech content privacy enforcement method can enhance the algorithm's robustness to attempt to recover the clean speech, at a slight cost of audio quality.
Audio watermarking aims to embed identifiable information into audio while remaining imperceptible. Existing methods adopt high-fidelity, low-energy designs to preserve perceptual quality, but the resulting watermarks lack robustness under suppression by speech reconstruction models. Improving robustness is challenging due to the inherent robustness-fidelity trade-off in existing designs, where increasing watermark energy improves robustness but reduces fidelity. To address this problem, we propose a feature-aligned watermarking method that aligns the watermark with the original speech feature distribution, allowing higher watermark energy to improve robustness while preserving imperceptibility. We use a pretrained speech codec to generate a pseudo-speech watermark and fuse it into the spectrogram of the input audio, with VAD loss and perceptual losses guiding embedding within voiced regions. Experiments show that our method maintains imperceptibility comparable to existing approaches while substantially improving robustness under both seen and unseen speech reconstruction models.
Speech recordings used for dementia detection inherently expose speaker identity, raising critical privacy concerns. Existing methods typically address only singular threats and fail to resolve the privacy--utility trade-off. We propose a multi-level framework designed to neutralize two distinct eavesdropping vectors. At the signal level, a Cumulative Signal Attack (CSA) concentrates perturbations in keyword-aligned regions to maximize transcription error (Word Error Rate WER = 1.00) while preserving vital prosodic biomarkers. At the feature level, a Gradient Reversal Layer (GRL) with Mutual Information (MI)-guided noise injection suppresses speaker-discriminative dimensions while retaining dementia-relevant diagnostic structure. Evaluated on the DementiaBank Pitt Corpus, our framework achieves near-chance speaker identification (Equal Error Rate EER = 0.59, F1 = 0.003) while maintaining strong dementia classification performance (F1 = 0.78, AUC = 0.86).
Henriette Flore Kenne, Raphael Anaadumba, Mohammad Arif Ul Alam
ADD in real-world scenarios has evolved from speech-only spoofing to more challenging component-level settings, where speech and environmental sounds may be independently manipulated. To tackle this, we propose EnvTriCascade, an Environment-Aware Tri-Stage Cascaded framework for the ESDD2 Challenge. First, a mix-consistency detector provides a binary prior to distinguish original recordings from manipulated mixtures, which calibrates the final decisions. Next, two complementary five-class detectors, leveraging SSLAM+XLS-R and EAT-large+XLS-R representations, extract robust multi-branch features integrated via a cross-branch attention-gated classifier. To enhance robustness against diverse mixing conditions, we incorporate RawBoost augmentation. Trained exclusively on the official CompSpoofV2 dataset, our system achieves a Macro-F1 score of 0.8266 on the test set, significantly outperforming the official baseline and ranking second in the challenge.