With the increasing popularity of smart wearable devices, such as earbuds and smart watches, presents new challenges for seamless and secure user authentication due to their limited user interfaces. Conventional biometric methods, including voice, fingerprints, and facial recognition often face issues such as usability limitations, interference from noise, or vulnerability to spoofing attacks. This paper introduces a novel authentication system called BaroAuth, which utilizes the stable and unique Speech-aware Pressure Sequences (SPSs) patterns captured by a miniaturized MEMS barometer embedded in earbuds. The design of BaroAuth hinges on two important observations. First, the production of speech relies on the coordinated movements of articulatory organs, including the tongue, jaw, and soft palate. These organs, through the activity of the temporomandibular joint (TMJ), alter the shape of the ear canal, thereby causing subtle pressure changes that encode the speaker's unique physiological characteristics and articulatory dynamics. Second, SPSs show significant intra-individual consistency and considerable inter-individual variability, which barometers can effectively measure. Meanwhile, we develop the BaroAuth prototype and carry out comprehensive experiments based on it. The experimental findings reveal that BaroAuth demonstrates a mean false-acceptance rate (FAR) of 0.41% and a false-rejection rate (FRR) of 1.23%, respectively, even under complex attack scenarios.

The growing adoption of smart wearable devices (e.g., earbuds and smart watches) poses new challenges for secure and seamless user authentication due to their limited interaction interfaces. Conventional biometric methods, including fingerprints, voice, and facial recognition, often suffer from usability constraints, noise interference, or susceptibility to spoofing attacks. In this paper, we propose BaroAuth, a novel authentication system that utilizes the stable and distinctive patterns of Speech-aware Pressure Sequences (SPSs) captured by miniature MEMS barometers embedded in earbuds. The design of BaroAuth is based on two key observations. First, speech production involves coordinated movements of articulatory organs, such as the jaw, tongue, and soft palate, which reshape the ear canal geometry via the temporomandibular joint (TMJ), generating subtle pressure variations that encode the speaker’s unique articulatory dynamics and physiological traits. Second, SPSs demonstrate strong intra-individual consistency and notable inter-individual variability, which can be effectively captured by barometers. We implement the prototype of BaroAuth and conduct comprehensive experiments on it. Experimental results demonstrate that BaroAuth achieves a mean false-acceptance rate (FAR) and false-rejection rate (FRR) of 1.62% and 1.74%, respectively, even under sophisticated attack scenarios.

Speech Emotion Recognition (SER), as a core technology in intelligent interaction and affective computing, faces critical privacy security challenges. Speech not only contains emotional information, but also encompasses sensitive data such as conversational content and speaker identity markers. The sensitive information could be potentially reconstructed through Automatic Speech Recognition (ASR), posing substantial privacy leakage risks. Existing privacy protection methods focus mainly on suppressing the leakage of demographic characteristics such as speaker identity, while there is a lack of systematic research on the speech content. The traditional SER and ASR joint training paradigm relies on the sequence alignment mechanism of ASR, which leads to deep coupling of emotion features with the corresponding text, causing a fundamental contradiction between “performance improvement” and “privacy protection”. To address this problem, this paper proposes an attentionbased cascaded learning enabling privacy-preserving (Aclipse) strategy, which is divided into two stages. In the first stage, the emotion-retaining (ER) module and semantics-obfuscating (SO) module in cascaded manner, which consist of the channel-level attention mechanism, guides the targeted injection of adversarial perturbation by identifying and strengthening high-frequency feature channels that are critical to ASR performance, thereby significantly improving the semantic obfuscation to achieve speech privacy protection while minimizing the negative impact on SER performance. In the second stage, we freeze the SO module parameters to maintain its interference effect on ASR, and fine-tune the ER to improve the performance of SER by adjusting the channel-level attention mechanism to focus on emotion-related information. The experimental results on the IEMOCAP dataset show that Aclipse achieves an effective balance between performance and privacy protection, providing a new idea to build a trustworthy SER system.