mlsanityData to 14 Oct 2026

Generalized End-to-End Loss for Speaker Verification

arXiv 1710.10467 ↗First posted 28 Oct 2017eess.AS cs.CL cs.LGcited by 293

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Cited by 20 in Diffusion models, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection. With these properties, our model with the new loss function decreases speaker verification EER by more than 10%, while reducing the training time by 60% at the same time. We also introduce the MultiReader technique, which allows us to do domain adaptation - training a more accurate model that supports multiple keywords (i.e. "OK Google" and "Hey Google") as well as multiple dialects.

Key facts. Cited by 293 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Oct 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 57; 2022: 64; 2023: 50; 2024: 56; 2025: 39; 2026: 27.

293 papers.

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  1. 2021YourTTS: Towards Zero-Shot Multi-Speaker TTS and Zero-Shot Voice Conversion for everyonecited by 278
  2. 2021AdaSpeech: Adaptive Text to Speech for Custom Voicecited by 98
  3. 2021Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generationcited by 93
  4. 2021Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidancecited by 84
  5. 2023AV-Deepfake1M: A Large-Scale LLM-Driven Audio-Visual Deepfake Datasetcited by 80
  6. 2022ContentVec: An Improved Self-Supervised Speech Representation by Disentangling Speakerscited by 74
  7. 2021Translatotron 2: High-quality direct speech-to-speech translation with voice preservationcited by 71
  8. 2022CVSS Corpus and Massively Multilingual Speech-to-Speech Translationcited by 71
  9. 2021Can You Hear It? Backdoor Attacks via Ultrasonic Triggerscited by 44
  10. 2021Neural Dubber: Dubbing for Videos According to Scriptscited by 39
  11. 2021Investigating on Incorporating Pretrained and Learnable Speaker Representations for Multi-Speaker Multi-Style Text-to-Speechcited by 37
  12. 2021MAAS: Multi-modal Assignation for Active Speaker Detectioncited by 36
38 more
  1. 2022Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Datacited by 35
  2. 2024Autoregressive Diffusion Transformer for Text-to-Speech Synthesiscited by 34
  3. 2021Turn-to-Diarize: Online Speaker Diarization Constrained by Transformer Transducer Speaker Turn Detectioncited by 32
  4. 2022Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparitiescited by 28
  5. 2023Intelligible Lip-to-Speech Synthesis with Speech Unitscited by 28
  6. 2022Towards Building Text-To-Speech Systems for the Next Billion Userscited by 27
  7. 2021SwissDial: Parallel Multidialectal Corpus of Spoken Swiss Germancited by 26
  8. 2025CARES: Comprehensive Evaluation of Safety and Adversarial Robustness in Medical LLMscited by 26
  9. 2021AVA-AVD: Audio-Visual Speaker Diarization in the Wildcited by 25
  10. 2021V2C: Visual Voice Cloningcited by 24
  11. 2021CLSRIL-23: Cross Lingual Speech Representations for Indic Languagescited by 22
  12. 2021Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speechcited by 22
  13. 2023DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embeddingcited by 22
  14. 2024ESPnet-SPK: full pipeline speaker embedding toolkit with reproducible recipes, self-supervised front-ends, and off-the-shelf modelscited by 22
  15. 2022Going In Style: Audio Backdoors Through Stylistic Transformationscited by 21
  16. 2024StyleDubber: Towards Multi-Scale Style Learning for Movie Dubbingcited by 21
  17. 2021Multimodal Emotion Recognition with High-level Speech and Text Featurescited by 20
  18. 2022Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chesscited by 18
  19. 2021AdaSpeech 2: Adaptive Text to Speech with Untranscribed Datacited by 17
  20. 2021Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularizationcited by 17
  21. 2023DiffProsody: Diffusion-based Latent Prosody Generation for Expressive Speech Synthesis with Prosody Conditional Adversarial Trainingcited by 16
  22. 2024DiarizationLM: Speaker Diarization Post-Processing with Large Language Modelscited by 16
  23. 2023UnitSpeech: Speaker-adaptive Speech Synthesis with Untranscribed Datacited by 15
  24. 2021Non-Autoregressive TTS with Explicit Duration Modelling for Low-Resource Highly Expressive Speechcited by 14
  25. 2021Voice Conversion Can Improve ASR in Very Low-Resource Settingscited by 14
  26. 2023SLMIA-SR: Speaker-Level Membership Inference Attacks against Speaker Recognition Systemscited by 14
  27. 2023AV2AV: Direct Audio-Visual Speech to Audio-Visual Speech Translation with Unified Audio-Visual Speech Representationcited by 14
  28. 2024TTSDS -- Text-to-Speech Distribution Scorecited by 14
  29. 2021"Hello, It's Me": Deep Learning-based Speech Synthesis Attacks in the Real Worldcited by 12
  30. 2021Emotional Prosody Control for Speech Generationcited by 12
  31. 2021More than Words: In-the-Wild Visually-Driven Prosody for Text-to-Speechcited by 12
  32. 2022Personal VAD 2.0: Optimizing Personal Voice Activity Detection for On-Device Speech Recognitioncited by 12
  33. 2025AudioGen-Omni: A Unified Multimodal Diffusion Transformer for Video-Synchronized Audio, Speech, and Song Generationcited by 12
  34. 2021Expressive Neural Voice Cloningcited by 11
  35. 2024The Impact of Speech Anonymization on Pathology and Its Limitscited by 11
  36. 2024Look Once to Hear: Target Speech Hearing with Noisy Examplescited by 11
  37. 2021High Fidelity Speech Regeneration with Application to Speech Enhancementcited by 10
  38. 2021Dr-Vectors: Decision Residual Networks and an Improved Loss for Speaker Recognitioncited by 10

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