Robust Speaker Identification System Based on Variational Bayesian Inference Gaussian Mixture Model and Feature Normalization

Hassan, Aliaa K.; Ahmed, Ahmed M.

doi:10.1007/978-981-16-2094-2_62

Cited by 1 publication

(2 citation statements)

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“…Mel-frequency cepstral coefficients (MFCCs) are widely used to extract features for voice-based authentication [ 13 , 14 , 15 , 16 , 17 , 18 ]. MFCCs are obtained by extracting features from the audio signal, and when used as input to the base model, they produce much better performance than when directly considering raw audio signals as input.…”

Section: Literature Reviewsmentioning

confidence: 99%

“…MFCCs are obtained by extracting features from the audio signal, and when used as input to the base model, they produce much better performance than when directly considering raw audio signals as input. As shown in Figure 2 , the typical speech processing method converts the speech signal from approximately 100 Hz to 5500 Hz into a sequence of MFCCs, and all features are then analyzed using a model such as a neural network or GMM [ 14 , 15 , 16 , 19 ]. However, excessive multiplication and the calculation process of MFCCs lead to high-capacity requirements of the system.…”

Section: Literature Reviewsmentioning

confidence: 99%

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ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation

Wong

Huang

Chen

et al. 2023

Sensors

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Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user’s identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings.

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