Data to 14 Oct 2026

Exploring wav2vec 2.0 on speaker verification and language identification

arXiv 2012.06185 ↗First posted 11 Dec 2020cs.SD cs.CL eess.AScited by 95

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Abstract (arXiv)

Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation learning. It follows a two-stage training process of pre-training and fine-tuning, and performs well in speech recognition tasks especially ultra-low resource cases. In this work, we attempt to extend self-supervised framework to speaker verification and language identification. First, we use some preliminary experiments to indicate that wav2vec 2.0 can capture the information about the speaker and language. Then we demonstrate the effectiveness of wav2vec 2.0 on the two tasks respectively. For speaker verification, we obtain a new state-of-the-art result, Equal Error Rate (EER) of 3.61% on the VoxCeleb1 dataset. For language identification, we obtain an EER of 12.02% on 1 second condition and an EER of 3.47% on full-length condition of the AP17-OLR dataset. Finally, we utilize one model to achieve the unified modeling by the multi-task learning for the two tasks.

Key facts. Cited by 95 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2020.

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Citing papers per year: 2021: 14; 2022: 26; 2023: 22; 2024: 16; 2025: 11; 2026: 6.

95 papers.

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2021SUPERB: Speech processing Universal PERformance Benchmarkcited by 666
2023Scaling Speech Technology to 1,000+ Languagescited by 411
2022Comparative layer-wise analysis of self-supervised speech modelscited by 158
2021Direct speech-to-speech translation with discrete unitscited by 149
2021Unsupervised Speech Recognitioncited by 148
2022GCNet: Graph Completion Network for Incomplete Multimodal Learning in Conversationcited by 87
2021A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understandingcited by 82
2021Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representationscited by 81
2022SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilitiescited by 77
2023What Do Self-Supervised Speech Models Know About Words?cited by 64
2021Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognitioncited by 49
2024A Large-Scale Evaluation of Speech Foundation Modelscited by 40

38 more

2021Fine-tuning wav2vec2 for speaker recognitioncited by 39
2022Exploration of A Self-Supervised Speech Model: A Study on Emotional Corporacited by 31
2022Self-Supervised Representation Learning for Speech Using Visual Grounding and Masked Language Modelingcited by 25
2022FitHuBERT: Going Thinner and Deeper for Knowledge Distillation of Speech Self-Supervised Learningcited by 25
2021S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representationscited by 23
2021Conformer-Based Self-Supervised Learning for Non-Speech Audio Taskscited by 22
2021Improved Language Identification Through Cross-Lingual Self-Supervised Learningcited by 20
2022Opening the Black Box of wav2vec Feature Encodercited by 18
2023What Can an Accent Identifier Learn? Probing Phonetic and Prosodic Information in a Wav2vec2-based Accent Identification Modelcited by 18
2024Coding Speech through Vocal Tract Kinematicscited by 18
2022Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognitioncited by 17
2024Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing Detectioncited by 17
2023Let There Be Sound: Reconstructing High Quality Speech from Silent Videoscited by 12
2022Improving Mispronunciation Detection with Wav2vec2-based Momentum Pseudo-Labeling for Accentedness and Intelligibility Assessmentcited by 10
2022Fully Automated End-to-End Fake Audio Detectioncited by 10
2021Multi-task Voice Activated Framework using Self-supervised Learningcited by 9
2022A Comparative Study of Self-supervised Speech Representation Based Voice Conversioncited by 9
2022Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognitioncited by 9
2025From Faces to Voices: Learning Hierarchical Representations for High-quality Video-to-Speechcited by 9
2022Anti-Spoofing Using Transfer Learning with Variational Information Bottleneckcited by 8
2023Knowledge-aware Bayesian Co-attention for Multimodal Emotion Recognitioncited by 8
2024SLIM: Style-Linguistics Mismatch Model for Generalized Audio Deepfake Detectioncited by 8
2021Pretext Tasks selection for multitask self-supervised speech representation learningcited by 7
2023Improving Speech Inversion Through Self-Supervised Embeddings and Enhanced Tract Variablescited by 7
2023Automatic Pronunciation Assessment -- A Reviewcited by 7
2022Accidental Learners: Spoken Language Identification in Multilingual Self-Supervised Modelscited by 6
2023Leveraging ASR Pretrained Conformers for Speaker Verification through Transfer Learning and Knowledge Distillationcited by 6
2024Singer Identity Representation Learning using Self-Supervised Techniquescited by 6
2024A Layer-Anchoring Strategy for Enhancing Cross-Lingual Speech Emotion Recognitioncited by 6
2022Robust Speaker Recognition with Transformers Using wav2vec 2.0cited by 5
2024SKILL: Similarity-aware Knowledge distILLation for Speech Self-Supervised Learningcited by 5
2022Large-scale learning of generalised representations for speaker recognitioncited by 4
2023Label Aware Speech Representation Learning For Language Identificationcited by 4
2023Comparative Analysis of the wav2vec 2.0 Feature Extractorcited by 4
2024Towards Supervised Performance on Speaker Verification with Self-Supervised Learning by Leveraging Large-Scale ASR Modelscited by 4
2022Introducing Semantics into Speech Encoderscited by 3
2023Towards spoken dialect identification of Irishcited by 3
2024ELP-Adapters: Parameter Efficient Adapter Tuning for Various Speech Processing Taskscited by 3

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