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eess.AS updates on arXiv.org

Dependence on Early and Late Reverberation of Single-Channel Speaker Distance Estimation MIST: Multimodal Interactive Speech-based Tool-calling Conversational Assistants for Smart Homes LiVeAction: a Lightweight, Versatile, and Asymmetric Neural Codec Design for Real-time Operation Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization PairAlign: A Framework for Sequence Tokenization via Self-Alignment with Applications to Audio Tokenization WavCube: Unifying Speech Representation for Understanding and Generation via Semantic-Acoustic Joint Modeling Predictive-Generative Drift Decomposition for Speech Enhancement and Separation Minimizing Modality Gap from the Input Side: Your Speech LLM Can Be a Prosody-Aware Text LLM X-Voice: Enabling Everyone to Speak 30 Languages via Zero-Shot Cross-Lingual Voice Cloning JASTIN: Aligning LLMs for Zero-Shot Audio and Speech Evaluation via Natural Language Instructions Phoneme-Level Deepfake Detection Across Emotional Conditions Using Self-Supervised Embeddings When Audio-Language Models Fail to Leverage Multimodal Context for Dysarthric Speech Recognition Dimensionality-Aware Anomaly Detection in Learned Representations of Self-Supervised Speech Models Mitigating Multimodal LLMs Hallucinations via Relevance Propagation at Inference Time Virtual Speech Therapist: A Clinician-in-the-Loop AI Speech Therapy Agent for Personalized and Supervised Therapy LASE: Language-Adversarial Speaker Encoding for Indic Cross-Script Identity Preservation Towards Improving Speaker Distance Estimation through Generative Impulse Response Augmentation Beyond Decodability: Reconstructing Language Model Representations with an Encoding Probe MMAudioReverbs: Video-Guided Acoustic Modeling for Dereverberation and Room Impulse Response Estimation Alethia: A Foundational Encoder for Voice Deepfakes From Birdsong to Rumbles: Classifying Elephant Calls with Out-of-Species Embeddings Beyond the Baseband: Adaptive Multi-Band Encoding for Full-Spectrum Bioacoustics Classification Predicting Upcoming Stuttering Events from Three-Second Audio: Stratified Evaluation Reveals Severity-Selective Precursors, and the Model Deploys Fully On-Device The False Resonance: A Critical Examination of Emotion Embedding Similarity for Speech Generation Evaluation DiffAnon: Diffusion-based Prosody Control for Voice Anonymization Recurrence-Based Nonlinear Vocal Dynamics as Digital Biomarkers for Depression Detection from Conversational Speech One Voice, Many Tongues: Cross-Lingual Voice Cloning for Scientific Speech Similarity Choice and Negative Scaling in Supervised Contrastive Learning for Deepfake Audio Detection Walking Through Uncertainty: An Empirical Study of Uncertainty Estimation for Audio-Aware Large Language Models Praxy Voice: Voice-Prompt Recovery + BUPS for Commercial-Class Indic TTS from a Frozen Non-Indic Base at Zero Commercial-Training-Data Cost
Using Inaudible Audio and Voice Assistants to Transmit Se...
Zhengxian He, Mohit Narayan Rajput, Mustaque Ahamad · 2020-09-22 · via eess.AS updates on arXiv.org

New security and privacy concerns arise due to the growing popularity of voice assistant (VA) deployments in home and enterprise networks. A number of past research results have demonstrated how malicious actors can use hidden commands to get VAs to perform certain operations even when a person may be in their vicinity. However, such work has not explored how compromised computers that are close to VAs can leverage the phone channel to exfiltrate data with the help of VAs. After characterizing the communication channel that is set up by commanding a VA to make a call to a phone number, we demonstrate how malware can encode data into audio and send it via the phone channel. Such an attack, which can be crafted remotely, at scale and at low cost, can be used to bypass network defenses that may be deployed against leakage of sensitive data. We use Dual-Tone Multi-Frequency tones to encode arbitrary binary data into audio that can be played over computer speakers and sent through a VA mediated phone channel to a remote system. We show that modest amounts of data can be transmitted with high accuracy with a short phone call lasting a few minutes. This can be done while making the audio nearly inaudible for most people by modulating it with a carrier with frequencies that are near the higher end of the human hearing range. Several factors influence the data transfer rate, including the distance between the computer and the VA, the ambient noise that may be present and the frequency of modulating carrier. With the help of a prototype built by us, we experimentally assess the impact of these factors on data transfer rates and transmission accuracy. Our results show that voice assistants in the vicinity of computers can pose new threats to data stored on such computers. These threats are not addressed by traditional host and network defenses. We briefly discuss possible mitigation ways.