arXiv AI

Child-Centric Voice Anonymization in Single and Multi-Speaker Speech via Domain-Adapted SSL Models

arXiv:2606. 29897v1 Announce Type: cross Abstract: Voice anonymization aims to protect speaker identity while preserving linguistic content and speech usability.

arXiv AI

Jul 14

Listen to the Features: Voice Anonymization Driven by Content Embedding Matching over Signal Reconstruction

arXiv:2607. 09767v1 Announce Type: cross Abstract: The paper presents a voice anonymization model focusing on preserving content rather than producing realistic speech.

By Adrien Schneider (M-PSI), Kacper Zabkowski (M-PSI), Anderson Augusma (M-PSI), Fr\'ed\'erique Letu\'e (SAM, SVH), Maria Camila Pinzon (M-PSI), Dominique Vaufreydaz (M-PSI)

rag diffusion efficiency

arXiv AI

Jul 7

NouveauVoice: Generating Novel Pseudo Speakers for Voice Anonymization

arXiv:2607. 03985v1 Announce Type: cross Abstract: Advanced neural technologies in speech synthesis and voice conversion (VC) have introduced severe risks to personal privacy, necessitating robust Speaker Anonymization Systems (SAS).

By Meiying Melissa Chen, Anastasia Kuznetsova, Zhenyu Wang, Zhiyao Duan

rag benchmarks safety

arXiv Machine Learning

Jun 30

BabyHuBERT: Multilingual Self-Supervised Learning for Segmenting Speakers in Child-Centered Long-Form Recordings

arXiv:2509. 15001v3 Announce Type: replace-cross Abstract: Child-centered daylong recordings are essential for studying early language development, but existing speech models trained on clean adult data perform poorly due to acoustic and linguistic differences.

By Th\'eo Charlot, Tarek Kunze, Maxime Poli, Alejandrina Cristia, Emmanuel Dupoux, Marvin Lavechin

arXiv Machine Learning

Jul 7

Deriving Benchmarking Datasets from Long-Form Recordings: Challenges and Opportunities

arXiv:2607. 03201v1 Announce Type: cross Abstract: Long-form recordings (LFRs) of child-centered audio are ecologically valid sources for studying early language development, but three problems limit their use.

By Kaveri K. Sheth, Lawrence Borst, Tarek Kunze, Marvin Lavechin, Okko R\"as\"anen, Sho Tsuji, Loann Peurey, Alix Bourr\'ee, Alejandrina Cristia

rag benchmarks safety

arXiv Machine Learning

Jun 2

Context-aware child-directed speech detection from long-form recordings

arXiv:2606. 01134v1 Announce Type: cross Abstract: Automatically distinguishing child-directed speech from adult-directed speech in long-form recordings is key to scalable analyses of children's language environments.

By Th\'eo Charlot, Tarek Kunze, Kaveri K. Sheth, Alejandrina Cristia, Marvin Lavechin

computer-vision fine-tuning

arXiv Machine Learning

Jun 9

A Comparison of SSL-Based Feature Extractors and Back-End Classifiers for Spoofing Detection: A Multi-Corpus Training and Cross-Linguistic Analysis

arXiv:2606. 08669v1 Announce Type: cross Abstract: Voice biometric systems face growing threats from spoofing attacks, yet the evaluation of detection models remains inconsistent across datasets.

By Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans

fine-tuning benchmarks safety

arXiv Machine Learning

Jun 9

Speaker-Invariant Representation Learning for Spoofing Detection via Gradient Reversal and A Variational Information Bottleneck

arXiv:2606. 08678v1 Announce Type: cross Abstract: Sophisticated generative speech technology can undermined the reliability of voice biometrics.

By Anh-Tuan Dao, Driss Matrouf, Mickael Rouvier, Nicholas Evans

robotics safety

arXiv AI

Jul 8

What Counts as Real? Speech Restoration and Voice Quality Conversion Pose New Challenges to Deepfake Detection

arXiv:2603. 14033v2 Announce Type: replace-cross Abstract: Audio anti-spoofing systems are typically trained to assign one authenticity label to an entire speech utterance.

By Shree Harsha Bokkahalli Satish, Harm Lameris, Joakim Gustafson, \'Eva Sz\'ekely

fine-tuning

arXiv AI

Jun 12

Towards Personalized Federated Learning for Dysarthric Speech Recognition

arXiv:2606. 13253v1 Announce Type: cross Abstract: Speech recognition is challenging for dysarthric speakers.

By Tao Zhong, Mengzhe Geng, Jiajun Deng, Shujie Hu, Xunying Liu

rag multimodal safety

arXiv AI

Jun 10

Linguistically Augmented Audio Speech Data (LinguAS)

arXiv:2606. 10246v1 Announce Type: cross Abstract: Maliciously-created fake speech, including deepfaked and spoofed audio, is proliferating at an alarming rate, and detection models are racing to stay ahead of the curve.

By Ashley R. Keaton, Zahra Khanjani, Christine Mallinson, Vandana P. Janeja

arXiv AI

Jul 10

PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction

arXiv:2607. 08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable.

By Wanyi Ning, Wei Zhou, Yingpeng Li, Yinshang Guo, Haitao Qian, Yiming Cheng

fine-tuning multimodal benchmarks

arXiv AI

Jun 10

Automated Pronunciation Evaluation for Korean Toddler Speech using Speech Diarization and Self-Supervised Learning

arXiv:2606. 10213v1 Announce Type: cross Abstract: Speech sound disorders affect approximately 44% of Korean pediatric communication disorder cases, yet automated assessment tools for Korean toddler speech remain underdeveloped.

By Diane Myung-kyung Woodbridge, Jee Hyun Suh

llms

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