• The Journal of the Acoustical Society of Korea
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• v.37 no.6
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• pp.483-488
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• 2018

Far field input utterance is one of the major causes of performance degradation of speaker verification systems. In this study, we used teacher student learning framework to compensate for the performance degradation caused by far field utterances. Teacher student learning refers to training the student deep neural network in possible performance degradation condition using the teacher deep neural network trained without such condition. In this study, we use the teacher network trained with near distance utterances to train the student network with far distance utterances. However, through experiments, it was found that performance of near distance utterances were deteriorated. To avoid such phenomenon, we proposed techniques that use trained teacher network as initialization of student network and training the student network using both near and far field utterances. Experiments were conducted using deep neural networks that input raw waveforms of 4-channel utterances recorded in both near and far distance. Results show the equal error rate of near and far-field utterances respectively, 2.55 % / 2.8 % without teacher student learning, 9.75 % / 1.8 % for conventional teacher student learning, and 2.5 % / 2.7 % with proposed techniques.

• Smart Media Journal
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• v.8 no.3
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• pp.23-30
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• 2019

With the popularization of the Internet and smartphone uses, people in the modern era are living in a multi-channel environment in which they access the information system freely through various methods and media. In the process of utilizing such services, users must authenticate themselves, the typical of which is ID & password authentication. It is considered the most convenient method as it can be authenticated only through the keyboard after remembering its own credentials. On the other hand, modern web services only allow passwords to be set with high complexity by different combinations. Passwords consisting of these complex strings also increase proportionally, since the more services users want to use, the more user authentication information they need to remember is recommended periodically to prevent personal information leakage. It is difficult for the blind, the disabled, or the elderly to remember the authentication information of users with such high entropy values and to use it through keyboard input. Therefore, this paper proposes a user authentication method using Google Assistant, MFCC and DTW algorithms and speaker authentication to provide the handicapped users with an easy user authentication method in the login process.