A review on state-of-the-art Automatic Speaker verification system from spoofing and anti-spoofing perspective

Current state-of-the-art speaker verification (SV) systems are known to be strongly affected by unexpected variability presented during testing, such as environmental noise or changes in vocal effort. In this work, we analyze and evaluate articulatory information of the tongue's movement as a means to improve the performance of speaker verification systems. We use a Spanish database, where besides the speech signals, we also include articulatory information that was acquired with an ultrasound system. Two groups of features are proposed to represent the articulatory information, and the obtained performance is compared to an SV system trained only with acoustic information. Our results show that the proposed features contain highly discriminative information, and they are related to speaker identity; furthermore, these features can be used to complement and improve existing systems by combining such information with cepstral coefficients at the feature level.

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Self-Attentive Multi-Layer Aggregation with Feature Recalibration and Deep Length Normalization for Text-Independent Speaker Verification System

Electronics ◽

10.3390/electronics9101706 ◽

2020 ◽

Vol 9 (10) ◽

pp. 1706

Author(s):

Soonshin Seo ◽

Ji-Hwan Kim

Keyword(s):

State Of The Art ◽

Speaker Verification ◽

Model Parameters ◽

Equal Error Rate ◽

Layer Depth ◽

Verification System ◽

Evaluation Dataset ◽

Representational Power ◽

Fully Connected ◽

Text Independent Speaker Verification

One of the most important parts of a text-independent speaker verification system is speaker embedding generation. Previous studies demonstrated that shortcut connections-based multi-layer aggregation improves the representational power of a speaker embedding system. However, model parameters are relatively large in number, and unspecified variations increase in the multi-layer aggregation. Therefore, in this study, we propose a self-attentive multi-layer aggregation with feature recalibration and deep length normalization for a text-independent speaker verification system. To reduce the number of model parameters, we set the ResNet with the scaled channel width and layer depth as a baseline. To control the variability in the training, we apply a self-attention mechanism to perform multi-layer aggregation with dropout regularizations and batch normalizations. Subsequently, we apply a feature recalibration layer to the aggregated feature using fully-connected layers and nonlinear activation functions. Further, deep length normalization is used on a recalibrated feature in the training process. Experimental results using the VoxCeleb1 evaluation dataset showed that the performance of the proposed methods was comparable to that of state-of-the-art models (equal error rate of 4.95% and 2.86%, using the VoxCeleb1 and VoxCeleb2 training datasets, respectively).

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Spoofing Speaker Verification System by Adversarial Examples Leveraging the Generalized Speaker Difference

Security and Communication Networks ◽

10.1155/2021/6664578 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Hongwei Luo ◽

Yijie Shen ◽

Feng Lin ◽

Guoai Xu

Keyword(s):

Neural Networks ◽

Loss Function ◽

Deep Neural Networks ◽

State Of The Art ◽

Speaker Verification ◽

Signal To Noise Ratio ◽

The State ◽

Verification System ◽

Adversarial Examples ◽

Human Hearing

Speaker verification system has gained great popularity in recent years, especially with the development of deep neural networks and Internet of Things. However, the security of speaker verification system based on deep neural networks has not been well investigated. In this paper, we propose an attack to spoof the state-of-the-art speaker verification system based on generalized end-to-end (GE2E) loss function for misclassifying illegal users into the authentic user. Specifically, we design a novel loss function to deploy a generator for generating effective adversarial examples with slight perturbation and then spoof the system with these adversarial examples to achieve our goals. The success rate of our attack can reach 82% when cosine similarity is adopted to deploy the deep-learning-based speaker verification system. Beyond that, our experiments also reported the signal-to-noise ratio at 76 dB, which proves that our attack has higher imperceptibility than previous works. In summary, the results show that our attack not only can spoof the state-of-the-art neural-network-based speaker verification system but also more importantly has the ability to hide from human hearing or machine discrimination.

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