# U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
###### Abstract
AM-MobileNet1D [12] further demonstrates that 1D-CNN architectures are sufficient for identifying features from raw audio waveforms. Also, the architecture requires fewer parameters compared to SincNet. Although supervised speaker recognition architectures perform excellently in recognition tasks on a large set of speakers. However, DL strategies require a vast amount of labeled data to operate on speech-related queries. Generating speech embeddings has been widely observed in the speaker recognition domain [13; 14; 15]. Embedding refers to generating vectors of continuous values. Often, architectures using speaker embeddings are also termed stage-wise architectures [7]. Currently, unsupervised speaker recognition systems implement domain adaptation policies, mostly fused with embedding vectors [7; 16]. Domain adaptation refers to finding appropriate similarities, where a framework is trained on training data, and tested on a similar yet unseen data. Hence, domain adaptation strategies may perform poorly when the variation of training and the unseen dataset is massive. Further, domain adaptation strategies may also produce less accuracy if trained on an inadequate dataset with minimal variation [15]. Yet, efforts have been made to reduce the interpretation of unseen data over training data, by adding adversarial training [17], improving training policies [18], covariance adaptation policies [19], etc. Although these policies improve the robustness, most strategies are still prone to various speech diversions, such as language, speech pattern, age, emotion, and so on. Currently, in the aspect of DL, embeddings can be generated using triplet [20] and pairwise loss [21] techniques. In triplet loss architecture, three parallel inputs flow through the network: anchor, negative, and positive. The positive input contains a similar class w.r.t. to the anchor, whereas the negative input contains a different class. Comparatively, in pairwise architecture, a pair of information flows either belonging to a single or different class. Triplet architectures have been perceived in speaker feature extraction in supervised practice [22]. This paper introduces an unsupervised strategy of generating speaker embedding directly from the unseen data. Hence, the method does not depend on domain adaptation policies and can adapt diverse features from most speech data. Moreover, we insist on converting DL architecture's training process to both semi-supervised and unsupervised manners. Yet, to do so, the system requires segmented audio streams (length of 1 s) and needs to guarantee that a segment contains only one person's speech. The audio segment is further windowed into smaller speech frames (0.2 s) for training the DL architecture. The audio segments are assigned pseudo labels, which are further reconstructed by DL architecture. Figure 1 illustrates the construction of the training procedure. The overall contributions of the paper are the following:  
* We introduce a strategy of generating speaker-dependent embeddings, named u-vector. The training process is domain-independent and directly learns from the unlabeled data. * We use pairwise constraints and non-generative augmentations to train AutoEmbedder architecture. * We explore the possibilities of our strategy in both unsupervised and semi-supervised training criteria. * We evaluate the proposed policy with two inter-cluster based strategies: triplet and pairwise architectures. * We finally conclude that a DL architecture can discriminate speakers from pseudo labels based on feature similarity. We organize the paper as follows, Section 2 reviews the related works conducted in speaker recognition domain. Section 3 clarifies the construction of the training procedure, along with the challenges, and modifications. Section 4 illustrates the experimental setup, datasets and the analysis of the architecture's performance. In Section 5, we sketch the proposed method's future initiatives along with usabilities. Finally, Section 6 concludes the paper.