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Ordered and Binary Speaker Embedding

arXiv 2305.16043 ↗First posted 25 May 2023cs.SD cs.LG eess.AScited by 0

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Abstract (arXiv)

Modern speaker recognition systems represent utterances by embedding vectors. Conventional embedding vectors are dense and non-structural. In this paper, we propose an ordered binary embedding approach that sorts the dimensions of the embedding vector via a nested dropout and converts the sorted vectors to binary codes via Bernoulli sampling. The resultant ordered binary codes offer some important merits such as hierarchical clustering, reduced memory usage, and fast retrieval. These merits were empirically verified by comprehensive experiments on a speaker identification task with the VoxCeleb and CN-Celeb datasets.

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References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Squeeze-and-Excitation Networkscited by 4,763
  3. 2017Neural Discrete Representation Learningcited by 4,650
  4. 2015Deep Learning and the Information Bottleneck Principlecited by 793
  5. 2018Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networkscited by 101
  6. 2014Learning Ordered Representations with Nested Dropoutcited by 81
  7. 2018Exploring the Encoding Layer and Loss Function in End-to-End Speaker and Language Recognition Systemcited by 45
  8. 2020VoxSRC 2020: The Second VoxCeleb Speaker Recognition Challengecited by 26
  9. 2020CN-Celeb: multi-genre speaker recognitioncited by 25
  10. 2021Progressive Neural Image Compression with Nested Quantization and Latent Orderingcited by 24
  11. 2021Anytime Sampling for Autoregressive Models via Ordered Autoencodingcited by 11
  12. 2022The 2021 NIST Speaker Recognition Evaluationcited by 10
4 more
  1. 2022VoxSRC 2021: The Third VoxCeleb Speaker Recognition Challengecited by 9
  2. 2019A PCA-like Autoencodercited by 7
  3. 2017Deep Speaker Feature Learning for Text-independent Speaker Verificationcited by 6
  4. 2015Binary Speaker Embeddingcited by 1

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