Home IoT device certification through speaker recognition

Shin, Dong-Gyu; Jun, Moon-Seog

doi:10.1109/icact.2015.7224867

Cited by 14 publications

(10 citation statements)

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“…Studies on speaker verification [20]- [26] compare the voice of a speaker with that of a pre-registered person for authentication. Speaker verification is used for Internet of things (IoT) device authentication [27], network security [28], and user authentication [29]. For example, the literature [26] combines mel-frequency cepstral coefficients (MFCC) and linear predictive coding (LPC) to improve the performance of speaker verification for low-quality input speech signals.…”

Section: A Speaker Recognition Using Stationary Devicementioning

confidence: 99%

Speaker Identification for Business-Card-Type Sensors

Yamaguchi

Oshima

et al. 2021

IEEE Open J. Comput. Soc.

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Section: A Speaker Recognition Using Stationary Devicementioning

confidence: 99%

Speaker Identification for Business-Card-Type Sensors

Yamaguchi

Oshima

et al. 2021

IEEE Open J. Comput. Soc.

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“…To the best of our knowledge, there are only two researchers who adopted voice biometrics as an authentication mechanism for the IoT ecosystem. Shin and Jun [33] have implemented voice recognition technology to verify authorized users for controlling and monitoring an automated home environment. The researcher proposed a voice recognition system that is divided into server and device parts.…”

Section: Related Workmentioning

confidence: 99%

Untitled

2020

IJSPTM

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Behavioral biometric authentication is considered as a promising approach to securing the internet of things (IoT) ecosystem. In this paper, we investigated the need and suitability of employing voice recognition systems in the user authentication of the IoT. Tools and techniques used in accomplishing voice recognition systems are reviewed, and their appropriateness to the IoT environment are discussed. In the end, a voice recognition system is proposed for IoT ecosystem user authentication. The proposed system has two phases. The first being the enrollment phase consisting of a pre-processing step where the noise is removed from the voice for the enrollment process, the feature extraction step where feature traits are extracted from user's voice, and the model training step where the voice model is trained for the IoT user. And the second being the phase verifies whether the identity claimer is the owner of the IoT device. Based on the resources limitedness of the IoT technologies, the suitability of text-dependent voice recognition systems is promoted. Likewise, the use of MFCC features is considered in the proposed system.

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“…As research in home IoT intensifies, many studies are being done on user recognition. Although user recognition using image processing such as facial recognition and also voice recognition [7,18,19] has been extensively studied, problems exist that are likely to cause errors depending on the conditions, including camera angle, the shape of the user’s head, and the user’s physical condition. Many studies have addressed these concerns, including studies on proximity-based neighbor identification protocol (PNIP) [12,20], which detects a low frequency device and transmits an advertising signal with the user’s ID, and radio-frequency identification passive (RFID passive) [21], a system that uses specific device and radio frequency (RF) to identify the user.…”

Section: Related Researchmentioning

confidence: 99%

“…In particular, smart-home technology for the elderly has the potential to improve the quality of life in an aging society. IoT smart-home service for specific users such as senior citizens and dementia patients [6,7] should be able to distinguish between seniors who need customized services and other family members who need only regular services. Therefore, it is necessary to accurately determine who is in need of a service in real time and to provide enhanced services accordingly.…”

Section: Introductionmentioning

confidence: 99%

Real-Time User Identification and Behavior Prediction Based on Foot-Pad Recognition

Heo

Jeong

Kang

2019

Sensors

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In the IoT (Internet of things)-based smart home, the technology for recognizing individual users among family members is very important. Although research in areas such as image recognition, biometrics, and individual wireless devices is very active, these systems suffer from various problems such as the need to follow an intentional procedure or own a specific device. Furthermore, with a centralized server system for IoT service, it is difficult to guarantee real-time determinism with high accuracy. To overcome these problems, we suggest a method of recognizing users in real time from the foot pressure characteristics measured as a user steps on a footpad. The proposed model in this paper uses a preprocessing algorithm to determine and generalize the angle of foot pressure. Based on this generalized foot pressure angle, we extract nine features that can distinguish individual human beings, and employ these features in user-recognition algorithms. Performance evaluation of the model was conducted by combining two preprocessing algorithms used to generalize the angle with four user-recognition algorithms.

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Home IoT device certification through speaker recognition

Cited by 14 publications

References 0 publications

Speaker Identification for Business-Card-Type Sensors

Speaker Identification for Business-Card-Type Sensors

Untitled

Real-Time User Identification and Behavior Prediction Based on Foot-Pad Recognition

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