# U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data
###### Abstract  
Speaker recognition deals with recognizing speakers by their speech. Most speaker recognition systems are built upon two stages, the first stage extracts low dimensional correlation embeddings from speech, and the second performs the classification task. The robustness of a speaker recognition system mainly depends on the extraction process of speech embeddings, which are primarily pre-trained on a large-scale dataset. As the embedding systems are pre-trained, the performance of speaker recognition models greatly depends on domain adaptation policy, which may reduce if trained using inadequate data. This paper introduces a speaker recognition strategy dealing with unlabeled data, which generates clusterable embedding vectors from small fixed-size speech frames. The unsupervised training strategy involves an assumption that a small speech segment should include a single speaker. Depending on such a belief, a pairwise constraint is constructed with noise augmentation policies, used to train AutoEmbedder architecture that generates speaker embeddings. Without relying on domain adaption policy, the process unsupervised produces clusterable speaker embeddings, termed unsupervised vectors (u-vectors). The evaluation is concluded in two popular speaker recognition datasets for English language, TIMIT, and LibriSpeech. Also, a Bengali dataset is included to illustrate the diversity of the domain shifts for speaker recognition systems. Finally, we conclude that the proposed approach achieves satisfactory performance using pairwise architectures. speaker recognition; clustering; twin networks; deep learning Article  
U-Vectors: Generating Clusterable Speaker Embedding from Unlabeled Data  
[PERSON]. [PERSON], M.A. M.  
engines, speech-to-text processing, etc. Further, numerous automation systems on speech are currently being developed. A speech denoising mechanism removes environmental sounds from speech audio, helping to provide a clean speech [2]. Speech synthesis systems artificially enable computers to produce speech sounds [3], helping computers to communicate with humans. Speech emotion systems further extract human emotions from speech [4]. Thus, computers can apprehend human feelings. Speech segmentation systems can segment a speech into word/phone levels [5], helping to identify words and phones from speech. Further, computers can help humans in developing pronunciation [6]. Among the various speech-based solutions, speaker recognition has fascinating usage of identifying users by hearing speech. Speaker recognition systems are directly involved with biometric identification systems and are suitable for authenticating users remotely by hearing voices. In perspective to various biometric systems, such as facial recognition, fingerprint matching, and so on, speaker recognition also has vast usability in numerous domains, including telecom, banking, search optimization, and diarization [7]. Nevertheless, speaker recognition systems suffer difficulties, including speech states, emotional conditions, environmental noise, health conditions, speaking styles, etc. Further, in comparison with supervised speaker recognition approaches, unsupervised and semi-supervised strategies are hardly investigated [8]. Unsupervised and semi-supervised systems resolve the requirement of labeling a vast quantity of speech data. DL architectures have been extensively investigated for supervised speaker recognition systems. For speaker and speech recognition models, speech spectrograms and mel-frequency cepstral coefficients (MFCC) [9] are used as a preprocessing strategy. For such cases, convolutional neural networks (CNN) are generally implemented [10]. However, current architectures processes raw-audio and extract speaker recognizable features. SincNet [11] improves the feature extraction process from raw audio waves. The architecture fuses sinc functions with CNN that can extract speaker information from low and high cutoff frequencies.