Speaker recognition based on deep learning: An overview
Open accessAbstract
Speaker recognition is a task of identifying persons from their voices. Recently, deep learning has dramatically revolutionized speaker recognition. However, there is lack of comprehensive reviews on the exciting progress. In this paper, we review several major subtasks of speaker recognition, including speaker verification, identification, diarization, and robust speaker recognition, with a focus on deep-learning-based methods. Because the major advantage of deep learning over conventional methods is its representation ability, which is able to produce highly abstract embedding features from utterances, we first pay close attention to deep-learning-based speaker feature extraction, including the inputs, network structures, temporal pooling strategies, and objective functions respectively, which are the fundamental components of many speaker recognition subtasks. Then, we make an overview of speaker diarization, with an emphasis of recent supervised, end-to-end, and online diarization. Finally, we survey robust speaker recognition from the perspectives of domain adaptation and speech enhancement, which are two major approaches of dealing with domain mismatch and noise problems. Popular and recently released corpora are listed at the end of the paper.
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Automatic speaker verification (ASV) systems are vulnerable to spoofing attacks in which an adversary attempts to masquerade as another individual through the presentation of manipulated speech data.
“B IOMETRIC systems implemented with automatic speaker verification (ASV) technology [1] are vulnerable to spoofing attacks [2] whereby an adversary attempts to masquerade as another individual through the presentation of manipulated speech data [3].”
From ASVspoof 2021: Towards Spoofed and Deepfake Speech Detection in the Wild (2023) · cites this paper as [1]
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- Authors
- Bai, Z., & Zhang, X.-L.
- Affiliations
- Northwestern Polytechnical University (CN)
- Corresponding author
- Zhang, Xiao-Lei
- Venue
- Neural Networks
- Publisher
- Elsevier BV
- Published
- 2021-08
- Volume
- 140
- Pages
- 65-99
- ISSN
- 0893-6080 (ISSN-L)
- OpenAlex
- W3137249133
- Cited by
- 386
- References
- 329
- Citations by year
- 2026: 27 · 2025: 73 · 2024: 105 · 2023: 93 · 2022: 62 · 2021: 26
- FWCI
- 25.27
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Neural Networks (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, public-domain)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- speaker recognition, speaker verification, speaker identification, speaker diarization, robust speaker recognition, deep learning, speaker embedding, temporal pooling, domain adaptation, speech enhancement, domain mismatch, noise robustness, speech corpus
- MeSH terms
- Deep Learning, Noise, Speech Recognition Software
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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