User Cognition Antecedents of Smart Assistant Systems in Cars

Keller, Katharina; Jöntgen, Hendrik; Abdel-Karim, Benjamin M.; Hinz, Oliver

doi:10.1109/tits.2022.3216029

Cited by 1 publication

(2 citation statements)

References 93 publications

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“…Among them, the total number of initially chosen frames is n. By observing the distribution of LFAE, the high-energy frames of the real voice are concentrated in [1,2], while the replayed voice is scattered in [2,10]. In addition, there is a large gap between high-energy frames of the replayed voice.…”

Section: Voice Frame Selectionmentioning

confidence: 99%

“…For example, through voice interaction technology, smart home devices can respond to user instructions, control lighting, electrical appliances, etc., and live an intelligent life. With the development of voice recognition technology, voice interaction is also used in more complex tasks, such as with digital assistants in smartphones, cars, and other devices 1,2 . Voice authentication can make the user experience better, but it is vulnerable to spoofing attacks.…”

Section: Introductionmentioning

confidence: 99%

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FL-VLD: A Novel Voice Liveness Detection Framework Based on Asynchronous Federated Learning

Zong,

Li,

Zhu

et al. 2023

Preprint

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Voice Liveness Detection (VLD) has become one of the hot research topics in the Internet of Things era. Conventional VLD methods are centralized solutions that are trained on abundant data collected from local clients and stored on the server, however, they have the risk of causing data islands and privacy leakage. To address this problem, we propose a novel word-level VLD framework based on asynchronous federated learning (FL) with pop noise, named FL-VLD. Structurally, FL-VLD takes the preprocessed voice for local model training and constructs a global model by only transmitting the learned weights after differential privacy with FL’s central server in an asynchronous manner. In addition, the local network of the framework incorporates the residual network and the spatial grouping enhancement module to optimize the complexity and accuracy of the global model. With the advantage of FL’s distributed structure, FL-VLD solves the data island problem in VLD scenario without threatening users’ privacy. Experimental results on the popular POCO dataset show that our proposal is clearly superior to the traditional centralized methods as well as overperforming other federated schemes in terms of fairness, stability, accuracy, and lightweightness. Further, for attacks involving far-field replay, synthesis, and conversion, FL-VLD has high generalisation capabilities. Finally, the ablation study attests to its efficacy.

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