Deep learning methods in speaker recognition: a review

Abstract

This paper summarizes the applied deep learning practices in the field of speaker recognition, both verification and identification. Speaker recognition has been a widely used field topic of speech technology. Many research works have been carried out and little progress has been achieved in the past 5-6 years. However, as deep learning techniques do advance in most machine learning fields, the former state-of-the-art methods are getting replaced by them in speaker recognition too. It seems that DL becomes the now state-of-the-art solution for both speaker verification and identification. The standard x-vectors, additional to i-vectors, are used as baseline in most of the novel works. The increasing amount of gathered data opens up the territory to DL, where they are the most effective.

References in corpus (10)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models
• VoxCeleb2: Deep Speaker Recognition
• VoxCeleb: a large-scale speaker identification dataset
• Voice Conversion from Unaligned Corpora using Variational Autoencoding Wasserstein Generative Adversarial Networks
• Multi-task Recurrent Model for Speech and Speaker Recognition
• Speaker Recognition using Deep Belief Networks
• Investigation of Using Disentangled and Interpretable Representations for One-shot Cross-lingual Voice Conversion
• Investigation of Using VAE for i-Vector Speaker Verification
• Statistical Models in Forensic Voice Comparison

Cited by in corpus (4)

• Improving Fairness in Speaker Recognition
• A Deep Reinforcement Learning Approach to Audio-Based Navigation in a Multi-Speaker Environment
• Cognitive architecture aided by working-memory for self-supervised multi-modal humans recognition
• A Deep Reinforcement Learning Approach for Audio-based Navigation and Audio Source Localization in Multi-speaker Environments