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

arXiv: https://arxiv.org/abs/2407.15188 · HTML: https://mlsanity.com/paper/2407.15188/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2024.

## Built on by (most cited first, 8 of 8)

- [Nes2Net: A Lightweight Nested Architecture for Foundation Model Driven Speech Anti-spoofing](https://mlsanity.com/paper/2504.05657/) (2025)
- [Scores Know Bobs Voice: Speaker Impersonation Attack](https://mlsanity.com/paper/2603.02781/) (2026)
- [Interpolating Speaker Identities in Embedding Space for Data Expansion](https://mlsanity.com/paper/2508.19210/) (2025)
- [Noise-Conditioned Mixture-of-Experts Framework for Robust Speaker Verification](https://mlsanity.com/paper/2510.18533/) (2025)
- [Hyperbolic Additive Margin Softmax with Hierarchical Information for Speaker Verification](https://mlsanity.com/paper/2601.19709/) (2026)
- [Causal Self-supervised Pretrained Frontend with Predictive Code for Speech Separation](https://mlsanity.com/paper/2504.02302/) (2025)
- [Robust Target Speaker Diarization and Separation via Augmented Speaker Embedding Sampling](https://mlsanity.com/paper/2508.06393/) (2025)
- [Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents](https://mlsanity.com/paper/2607.28165/) (2026)

## Abstract (arXiv)

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.