# Comparison of Modern Deep Learning Models for Speaker Verification
## 2 Related Works  
The number of publications on Automatic Speech Recognition (ASR), including speaker verification, is rapidly growing [11]. Real-world applications necessitate the development of automatic speaker verification systems, which would be capable of using short arbitrary (text-independent) utterances that might be captured in diverse settings, including time and equipment. These variables intensify the challenge of identifying specific unique voice characteristics of a speaker, which are inherently subject to variability [16; 17; 18]. Numerous studies have proposed innovative approaches to enhance speaker verification by exploring various neural network architectures and processing methods, in many cases utilizing open or self-generated datasets [19; 20; 21; 22]. For instance, paper [20] presents a novel network with a hierarchical processing method, where information about intonation as a high-level function is combined with low-level embedding functions. This improves the accuracy of the system, which is confirmed by experimental results on the VoxCeleb1 test data. Paper [21] investigates the relationship between visual speech cues, like lip movements, and audio speech, leading to the introduction of a cross-modal speech co-learning paradigm. Recognizing speakers based on non-English language utterances presents another significant challenge. Studies show that speaker verification performance declines when there is a language mismatch between training and testing data. To improve cross-language invariance, ref. [23] proposed a method of unsupervised adversarial discriminative domain adaptation. In this approach, the representative speech data from one language is adapted to align with the source domain of another language. Taking it a step further, the authors of [24] introduced the Siamese SpeakerNet network, able to verify speakers without being influenced by language, gender, or age differences and demonstrated superior performance compared to existing methods. In this study, we concentrate on open and accessible pre-trained speech models for speaker verification. Following a thorough analysis, we selected the following four state-of-the-art speech models for further investigation: Pyannote, WavLM, TitaNet and ECAPA [25; 26; 27; 28]. Several publications have discussed the results and capabilities of these language models. For instance, ref. [26] describes the WavLM speaker embedding model as versatile and suitable for a range of tasks, including speaker verification, speech recognition and diarization. The authors of WavLM assert that their model outperforms others, specifically ECAPA, in speaker verification tasks on the VoxCeleb dataset. Studies [3; 29] present the results of utilizing the Pyannote and ECAPA models in diarization tasks rather than verification tasks. The authors of [30] aim to review the recent progress in speaker embedding development and to perform an experimental benchmark experimental comparison among the state-of-the-art speaker representations for a speaker verification task. This study uses the VoxCeleb1 dataset for the evaluation of these models. The dataset used in the conducted experiment in this study is in the English language. This study performs training as well as evaluation, and they compare the EER metrics for various types of speaker embedding types, such as x-vectors, d-vectors, r-vectors and others. This study does not consider the inference time nor delves into why these embeddings perform well or not. Table 1 presents the results of the experiments of [30], including the EER scores. In [31], the authors aim to compare the speaker verification performance for children's and adult's speech. To achieve this, they use the GMM-based speaker verification system and PF-STAR dataset, which contains children's English speech utterances. Our study evaluates the effectiveness of current speaker verification models without additional modifications, emphasizing that the dataset size is not a critical factor for analysis [32; 33; 34].