Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning

TL;DR

This paper provides a comprehensive review of neural speaker representation learning, covering theoretical models, practical approaches, and open-source tools, to advance speaker recognition and related applications.

Contribution

It offers a systematic overview of both theoretical and practical aspects of neural speaker modeling, including recent advances and open-source resources.

Findings

Discussion of supervised and self-supervised speaker encoders

Comparison of open-source speaker modeling toolkits

Analysis of robustness and interpretability in speaker representations

Abstract

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks.