Text-Independent Speaker Recognition

Benchmarks archive 2025-07-28

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Most implemented papers archive 2025-07-28

6 shown of 6 papers with code (21 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Three-Dimensional Lip Motion Network for Text-Independent Speaker Recognition
  13 Oct 2020 6 repositories listed
  To this end, in this work, we present a novel end-to-end 3D lip motion Network (3LMNet) by utilizing the sentence-level 3D lip motion (S3DLM) to recognize speakers in both the text-independent and text-dependent…
• Temporal Dynamic Convolutional Neural Network for Text-Independent Speaker Verification and Phonemetic Analysis
  7 Oct 2021 1 repository listed
  The temporal dynamic model adapts itself to phonemes without explicitly given phoneme information during training, and results show the necessity to consider phoneme variation within utterances for more accurate and…
• SpeechNAS: Towards Better Trade-off between Latency and Accuracy for Large-Scale Speaker Verification
  18 Sep 2021 1 repository listed
  Recently, x-vector has been a successful and popular approach for speaker verification, which employs a time delay neural network (TDNN) and statistics pooling to extract speaker characterizing embedding from…
• Masked Proxy Loss For Text-Independent Speaker Verification
  9 Nov 2020 1 repository listed
  We further propose Multinomial Masked Proxy (MMP) loss to leverage the hardness of speaker pairs.
• Frame-level speaker embeddings for text-independent speaker recognition and analysis of end-to-end model
  12 Sep 2018 1 repository listed
  In this paper, we propose a Convolutional Neural Network (CNN) based speaker recognition model for extracting robust speaker embeddings.
• Unified Hypersphere Embedding for Speaker Recognition
  22 Jul 2018 1 repository listed
  Incremental improvements in accuracy of Convolutional Neural Networks are usually achieved through use of deeper and more complex models trained on larger datasets.