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Related Experiment Video

Updated: Nov 12, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Speaker recognition based on deep learning: An overview.

Zhongxin Bai¹, Xiao-Lei Zhang¹

¹Center of Intelligent Acoustics and Immersive Communications (CIAIC) and the School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an Shaanxi 710072, China.

Neural Networks : the Official Journal of the International Neural Network Society

|March 21, 2021

Summary

This summary is machine-generated.

This review covers deep learning advancements in speaker recognition tasks like verification and diarization. It details deep learning-based feature extraction and robust methods for noise and domain mismatch challenges.

Keywords:

Deep learning Robust speaker recognition Speaker diarization Speaker identification Speaker recognition Speaker verification

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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Published on: February 6, 2020

Area of Science:

Computer Science
Artificial Intelligence
Signal Processing

Background:

Speaker recognition identifies individuals by voice.
Deep learning has significantly advanced speaker recognition capabilities.
Comprehensive reviews on deep learning in speaker recognition are scarce.

Purpose of the Study:

To review major deep learning-based speaker recognition subtasks.
To focus on deep learning's representation ability for feature extraction.
To survey advancements in speaker diarization and robust speaker recognition.

Main Methods:

Detailed review of deep learning-based speaker feature extraction (inputs, networks, pooling, objectives).
Overview of speaker diarization, including supervised, end-to-end, and online methods.
Survey of robust speaker recognition techniques: domain adaptation and speech enhancement.

Main Results:

Deep learning excels in abstract feature extraction for speaker recognition.
Significant progress in supervised, end-to-end, and online speaker diarization.
Effective strategies for robust speaker recognition against noise and domain mismatch.

Conclusions:

Deep learning is pivotal for modern speaker recognition.
Future research directions include improved feature extraction and robust systems.
The review provides a foundational resource for the field.