# Comparison of Modern Deep Learning Models for Speaker Verification
## 1 Introduction  
Speaker verification is one of the important research areas in fast-growing modern machine learning applications, known as Speaker Recognition (SR). Two other SR sub-fields are speaker identification and speaker diarization [1, 2]. Speaker verification aims to determine whether the declared identity of the speaker is true or false. During speaker verification, the system typically compares the provided voice sample with a reference voiceprint (template) associated with the declared identity. this is known as a one-to-one mapping. Eventually, the system makes a decision indicating whether the speaker is verified (matches the provided identity) or not verified (does not match the provided identity). In contrast to verification, the task of speaker identification is to determine the identity of the speaker from a set of possible speakers. In the speaker identification process, the system compares a given voice sample with voiceprints from a database of known speakers (one-to-many mapping). Then, the system attempts to identify the speaker by selecting the closest match from the database. The main goal of speaker diarization is to divide the input audio recording into homogeneous segments corresponding to individual speakers. Traditionally, speaker diarization involves both segmentation (detecting speaker changes in an audio recording) and clustering (grouping speech segments corresponding to specific speakers based on voice characteristics). Unlike speaker verification and identification, speaker diarization can be approached using unsupervised learning [3]. Although this paper is dedicated to studying several state-of-the-art deep speaker embedding models primarily in the context of verification, the results may also have implications for the broader field of speaker recognition. Nowadays, speaker verification has become a crucial task in many applications, such as security, authentication, surveillance, forensics, multi-speaker tracking, personalized user interfaces, voice assistants, door access control systems and others [4; 5; 6; 7]. Nonetheless, the most important capability of speaker verification is the ability to identify and distinguish between different speakers accurately to be able to pinpoint a specific speaker. Speech is a time-varying signal that conveys information on multiple levels, including acoustic, semantic, emotional and anatomical. The inherent variability of speech, coupled with the fact that even the same individual cannot say a phrase identically twice, makes its analysis a complex challenge. This complexity poses significant challenges in designing speaker recognition and verification systems. Early speaker verification systems were constrained by the limitations of statistical methods, which resulted in restricted accuracy. It was only with the advent of machine learning that a significant improvement in performance was achieved [2]. In recent years, numerous new models have emerged in the field of NLP; however, the dominant trend in speech processing revolves around models based on speaker embedding technology. The essence of the speaker embeddings technique is to map the essential features of a speaker's voice into a numerical (vector) representation of constant dimensions in a multidimensional space [8; 9]. Over the subsequent years, the concept of speaker embeddings has been continuously developed, primarily aiming to measure and enhance performance under variable recording or processing conditions [10]. Speaker embeddings offer several advantages compared to traditional speech processing techniques, including greater compactness of voice representation, resilience to noise and recording condition changes and improved discrimination leading to enhanced performance in speech recognition tasks. Currently, speaker embedding technology can be applied to various tasks, including speaker recognition, verification, identification, diarization, tracking different speakers in a recording and detecting the presence of specific keywords [1; 11].