102 citations · 106 across the 12 of their papers we have counts for
13 papers
Is Word Error Rate Enough? Rethinking Privacy Evaluation in Speech with Entity-Aware Metrics
Anjana Rajasekhar, Jule Pohlhausen, Nayana Jacob Alappattu +1
As the use of smart devices continues to increase, their potential to capture sensitive speech content raises growing privacy concerns. It is therefore critical to develop techniqu…
From Read Speech to Spoken Digits: A Task-Specific Evaluation of Speech Privacy With Informed Attackers
Jule Pohlhausen, Anjana Rajasekhar, Anna Leschanowsky +1
Protecting speech privacy in real-life audio recordings is a growing concern. This contribution evaluates the effectiveness of three obfuscation techniques in protecting linguistic…
Assessing the Impact of Noise and Speech Enhancement on the Intelligibility of Speech Codecs
Lyonel Behringer, Anna Leschanowsky, Anjana Rajasekhar +2
Preserving speech intelligibility is a minimum requirement for speech codecs in communication. Recently, very low-bitrate neural codecs have gained interest for replacing classical…
Benchmarking Neural Speech Codec Intelligibility with SITool
Anna Leschanowsky, Kishor Kayyar Lakshminarayana, Anjana Rajasekhar +4
Speech intelligibility assessment is essential for evaluating neural speech codecs, yet most evaluation efforts focus on overall quality rather than intelligibility. Only a few pub…
You Are What You Say: Exploiting Linguistic Content for VoicePrivacy Attacks
Ünal Ege Gaznepoglu, Anna Leschanowsky, Ahmad Aloradi +4
Speaker anonymization systems hide the identity of speakers while preserving other information such as linguistic content and emotions. To evaluate their privacy benefits, attacks…
Transparent NLP: Using RAG and LLM Alignment for Privacy Q&A
Anna Leschanowsky, Zahra Kolagar, Erion Çano +4
The transparency principle of the General Data Protection Regulation (GDPR) requires data processing information to be clear, precise, and accessible. While language models show pr…
Expert-Generated Privacy Q&A Dataset for Conversational AI and User Study Insights
Anna Leschanowsky, Farnaz Salamatjoo, Zahra Kolagar +1
Conversational assistants process personal data and must comply with data protection regulations that require providers to be transparent with users about how their data is handled…
Examining the Interplay Between Privacy and Fairness for Speech Processing: A Review and Perspective
Anna Leschanowsky, Sneha Das
Speech technology has been increasingly deployed in various areas of daily life including sensitive domains such as healthcare and law enforcement. For these technologies to be eff…
Evaluating Privacy, Security, and Trust Perceptions in Conversational AI: A Systematic Review
Anna Leschanowsky, Silas Rech, Birgit Popp +1
Conversational AI (CAI) systems which encompass voice- and text-based assistants are on the rise and have been largely integrated into people's everyday lives. Despite their widesp…
Voice Anonymization for All -- Bias Evaluation of the Voice Privacy Challenge Baseline System
Anna Leschanowsky, Ünal Ege Gaznepoglu, Nils Peters
In an age of voice-enabled technology, voice anonymization offers a solution to protect people's privacy, provided these systems work equally well across subgroups. This study inve…
Experts-in-the-Loop: Establishing an Effective Workflow in Crafting Privacy Q&A
Zahra Kolagar, Anna Katharina Leschanowsky, Birgit Popp
Privacy policies play a vital role in safeguarding user privacy as legal jurisdictions worldwide emphasize the need for transparent data processing. While the suitability of privac…
Benchmark Dataset Dynamics, Bias and Privacy Challenges in Voice Biometrics Research
Casandra Rusti, Anna Leschanowsky, Carolyn Quinlan +3
Speaker recognition is a widely used voice-based biometric technology with applications in various industries, including banking, education, recruitment, immigration, law enforceme…
VoicePrivacy 2022 System Description: Speaker Anonymization with Feature-matched F0 Trajectories
Ünal Ege Gaznepoglu, Anna Leschanowsky, Nils Peters
We introduce a novel method to improve the performance of the VoicePrivacy Challenge 2022 baseline B1 variants. Among the known deficiencies of x-vector-based anonymization systems…