## 2 State of the Art
During training, the voice identification model learns to more effectively extract unique features of the speaker based on the data that the face can provide about the personality. The authors suggested a distillation method that includes a hyperparameter to prevent an over-dependence of the voice model on the face model, addressing overfitting. In testing on the CN-Celeb and VoxCeleb1 datasets, the system showed a 10-15% reduction in EER compared with traditional methods using only voice data. Authentication systems working with text-dependent data are actively studied using various databases. One of these is RedDots. In [9], attention was paid to speaker verification using short speech segments. The experiment was conducted on a subset of 100 speakers who pronounced numerical sequences. The authors focused on the first eight phrases common to all subjects. A four-level decomposition using a wavelet transform was applied to these segments, and the resulting features were then transferred to the neural network for classification. Using the Hilbert transform to improve the phase characteristics of the sound made it possible to achieve EER = 4.85%. In [10], the authors focus on using Gaussian Mixture Models (GMMs) with Maximum A Posteriori (MAP) adaptation and Nuisance Attribute Projection (NAP) to compensate for channel effects. The experiments were conducted on 10 standard phrases from the RedDots archive. The focus was on the tests for the \"Impostor Correct\" case, in which the non-target speaker pronounces the correct phrase. This test allows simulating situations when the attacker knows the correct text, which complicates the authentication task. Using bootstrap aggregation allowed the EER to further reduce the EER from 3.3% to 2.6%. The paper [11] proposes a method for text-dependent speaker verification based on hidden Markov models (HMMs) and i-vector. The experiments were conducted using the RedDots database. For the experiment, digit-specific HMM was used, which improved the quality of speech segmentation and reduced errors compared to other methods, such as x-vector. The authors also applied uncertainty normalization and LDA regularization. This approach allowed them to obtain the results EER = 1.52% for men and 1.77% for women (Table 1). There are several areas of research aimed at ensuring the confidentiality of biometric data during processing by a biometric system and the knowledge of machine learning models: the use of homomorphic encryption methods, fuzzy extractors (vault, commitment, embedder, etc. ), and neural fuzzy extractors, as well as cancelable biometrics. Homomorphic encryption allows us to perform calculations on encrypted data without revealing their contents but requires significant computing resources [21]. Fuzzy extractors allow you to restore encrypted keys even with small changes in biometric data, providing flexibility, but have limitations in resistance to more significant changes in patterns [22]. A neural fuzzy extractor, based on neural networks, provides high accuracy in converting biometrics into code, but its reliability directly depends on the quality of training and the amount of data [23]. To improve the performance of homomorphic encryption, an approach called asynchronous encryption is proposed [12]. Its use allowed for performing operations on encrypted data in parallel with the processing of other data, which led to a reduction in processing time. Also, by optimizing the work with preprocessed data, it was possible to achieve an increase in the accuracy of personality recognition. This approach made it possible to achieve a template matching time of 2.38 ms with a memory size for storing one face image of 32.8 KB.