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VoiceID: 딥러닝 기반 음성인식을 통한 모바일 보안 솔루션
현진(Jin Hyun),류동헌(Dongheon Ryu),정강민(Kangmin Jeong),이영주(Youngjoo Lee) 대한전자공학회 2021 대한전자공학회 학술대회 Vol.2021 No.6
In this paper, we introduce a mobile security solution named VoiceID, which utilizes lightweight deep neural networks (DNNs) for speech signal processing. The proposed VoiceID uses a new frame-level speaker embedding from the existing DNN model, which can be trained with the softmax cross entropy. The fine-tuning training is newly added with the triplet loss function, enhancing the quality of user-level identification. The word-level identification is also realized to provide the access right for each user, empowered by using the Cosine distance between bedded voice vectors. Evaluation results show that our VoiceID system successfully restricts 99.93% of unregistered users with simple yet efficient DNN-based processing steps, which can be used for the lightweight security solutions.