# U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
## 2 Related Work  
Speaker recognition has been a topic of interest over the past decades, and various systems have been proposed to solve the challenge. In the domain of speaker recognition,  
Figure 1: The figure illustrates a set of segmented speech with an unknown number of speakers (in the example, two speakers, p and q). Speech segments are windowed into smaller speech frames, assuming that all frames of a single speech segment belong to a single class. Further, a DL-based embedding system finds speech similarities (inter-segment similarity) and relations from speech segments. The process results in generating clusterable speaker embeddings. numerous techniques have been observed since late 2000. Among these, embedding architectures have been widely explored to extract the diversity of speech frames. Embedding models are often considered feature extractors, which can generate a speech-print (related to finger-print) of an individual. Hence, every individual's speech will remain closer in the embedding space, causing to create a cluster of embeddings from speech frames. Gaussian mixture model (GMM) supervector [23] (stacking mean vectors from GMM), and Joint factor analysis (JFA) [24] have been popularly integrated into the speaker recognition task. JFA merges the speaker-dependent and channel-dependent supervector and generates a new supervector based on the dependency. GMM and JFA were significantly accepted as feature extractors and implemented in various speaker recognition strategies. Later on, inspired by JFA, identity vector (i-vector) [25] was introduced. I-vector contributes to changing the channel-dependent supervectors and integrates speaker information within the supervectors. Hence, i-vector became more sensitive to speech variations and greatly accepted by the researchers. In most cases of JFA and i-vector, MFCC is widely implemented. MFCC is a linear cosine transform of a log power spectrum used to extract a sound's information. However, a lower MFCC with a lower cepstral coefficient returns only sound information, whereas the higher value of the coefficient represents speaker information as well [26]. Further, probabilistic linear discriminant analysis (PLDA) [27] is mostly used for implementing speaker verification and identification systems using i-vectors [13]. The present improvement of DL architectures has led to revisiting the speech embedding representation neural architecture perspective. Deep vector (d-vector) [14] is a mutated implementation of the speech frame embeddings using deep neural networks (DNN). The d-vector depends on the automated feature extraction process of DNN. The model's training process is supervised, and in the basic implementation of the d-vector, it is explored as a text-dependent system. After the training procedure, the softmax layer is left out, and the embeddings are extracted from the last hidden layer. Although the d-vector is based on DNN, further studies have been made using CNN architectures [28]. In the modified architecture, speech is converted into MEL coefficients, which are normalized and supplied to CNN. Moreover, extensive studies have been made to improve the basic d-vector to a text-independent unsupervised vector generation using domain adaptation [29]. The mechanism is split into two parts in the upgraded version: a DNN that extracts embeddings and a separately trained classifier that classifies speakers. These studies' limitation is that most of them require massive labelled data in the training procedure. Also, the embedding performance in the case of unseen speakers dramatically depends on the training data. As DNN architectures are dependent on the amount of training data, an improved strategy of the d-vector is proposed, named x-vector [15]. X-vectors are a modified version of d-vector, which depends on basic sound augmentation techniques, noise and reverberation. Further, the implementation highly motivates data augmentation usage and presents a decent accuracy improvement over i-vectors.