U-vectors: Generating clusterable speaker embedding from unlabeled data

TL;DR

This paper presents an unsupervised method for generating clusterable speaker embeddings from unlabeled speech data, improving robustness across diverse domains without relying on domain adaptation.

Contribution

Introduces u-vectors, an unsupervised approach to produce speaker embeddings from unlabeled data, reducing dependence on domain-specific training and adaptation.

Findings

Achieves satisfactory speaker recognition performance on multiple datasets.

Demonstrates robustness across different languages and domain shifts.

Uses pairwise architecture for effective unsupervised embedding generation.

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 unsupervisely 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.