# U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
## 5 Discussion  
The pairwise architecture with training strategy results in a good performance in the speaker recognition process. However, throughout the investigation, the architecture tends to have some issues that have to be considered. Firstly, training the architecture with lesser speech variation causes overfitting, observed while keeping \(speaker=25\). Secondly, as the augmentation procedure fuses noises, speech data with excessive noises may not generate a good result. The degradation of performance due to excessive noise is a common concern, and by using a denoising mechanism, the situation can be handled [2]. Further, as the system is fully segmentation dependent, the target lies in developing an optimal audio segmentation procedure. Resolving these challenges would benefit the architecture for a wide range of speaker recognition and evaluation usage.  
Apart from the limitations, the u-vector strategy requires no pre-training on large speaker datasets, which is often observed in i-vector, d-vector, and x-vector [7]. Further, the u-vector strategy requires comparatively less per-speaker data than the other embedding strategies mentioned above. In the case of augmentations, u-vector architecture does not require any labeled data, which is primarily observed in generative speaker recognition architectures [44]. In an overall perspective, the u-vector mitigates the requirement of labeled data to a minimum.  
Aside from implementing u-vectors in an unsupervised and semi-supervised manner, the strategy can be implemented in self-supervised learning. Although self-supervised learning has its various forms based on the domain, u-vector aligns with contrastive self-supervised learning strategies [45]. In general, self-supervised learning learns better model representation (weights of a model) from unlabeled data. Further, the trained model is used to train on a small quantity of labeled data. U-vectors can be used to initialize model representations in the first stage of self-supervised learning. Further, a classification method/layer can be equipped with the model for training the model on labeled data.  
Finally, industrial systems often can not label every data. However, if a large labeled dataset is required, then building a model can become costly. Therefore, unsupervised, semi/self-supervised learning in speaker recognition systems is expected to produce low-cost and attainable systems.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Impurity = 0**} & \multicolumn{4}{c}{**Impurity = 0.05**} & \multicolumn{4}{c}{**Impurity = 0.1**} \\ \cline{2-10}  & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** & **ACC** & **NMI** & **ARI** \\ \hline Speakers = 25 & & & & & & & & & \\ Train & 0.920 & 0.985 & 0.882 & 0.945 & 0.990 & 0.923 & 0.954 & 0.992 & 0.941 \\ Ground & 0.909 & 0.969 & 0.869 & 0.854 & 0.941 & 0.768 & 0.789 & 0.907 & 0.643 \\ \hline Speakers = 50 & & & & & & & & & \\ Train & 0.771 & 0.955 & 0.617 & 0.614 & 0.918 & 0.355 & 0.666 & 0.933 & 0.448 \\ Ground & 0.930 & 0.983 & 0.912 & 0.903 & 0.966 & 0.835 & 0.841 & 0.948 & 0.751 \\ \hline Speakers=100 & & & & & & & & & \\ Train & 0.506 & 0.899 & 0.179 & 0.536 & 0.906 & 0.224 & 0.561 & 0.914 & 0.270 \\ Ground & 0.906 & 0.977 & 0.871 & 0.836 & 0.956 & 0.753 & 0.727 & 0.918 & 0.647 \\ \hline Speakers = 150 & & & & & & & & & \\ Train & 0.487 & 0.880 & 0.149 & 0.486 & 0.900 & 0.150 & 0.536 & 0.912 & 0.226 \\ Ground & 0.885 & 0.973 & 0.836 & 0.714 & 0.926 & 0.687 & 0.671 & 0.908 & 0.568 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The table benchmarks the pairwise architecture in ASR dataset with four groups of speakers, 25, 50, 100, and 150. For each group of speakers, the table also considers three segmentation impurities, 0, 0.05, and 0.1 to illustrate the shortcomings of incorrect segmentation, for fully unsupervised speaker recognition strategy.