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Quan Wang

31 papers hereh-index 174.1k citations34 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author5
  • middle author17
  • last author4

Across the 26 of 31 papers where every author was matched, so the position is known.

fields
  • eess.AS25
  • cs.CL3
  • cs.LG1
  • cs.SD1
  • stat.ML1
same name
  • Quan Wang — 104 papers
  • Quan Wang — 15 papers, h 7
  • Quan Wang — 14 papers, h 8
  • Quan Wang — 11 papers, h 5
  • Quan Wang — 9 papers, h 27
  • Quan Wang — 9 papers, h 9

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172022
most citedLinks: A High-Dimensional Online Clustering Method

13 citations · 31 across the 12 of their papers we have counts for

collaborators
Showing 2017 · eess.ASShow all

4 papers · 2 filters

eess.AS2017★ 2 cited

Wavenet based low rate speech coding

W. Bastiaan Kleijn, Felicia S. C. Lim, Alejandro Luebs +4

Traditional parametric coding of speech facilitates low rate but provides poor reconstruction quality because of the inadequacy of the model used. We describe how a WaveNet generat…

eess.AS2017

Attention-Based Models for Text-Dependent Speaker Verification

F A Rezaur Rahman Chowdhury, Quan Wang, Ignacio Lopez Moreno +1

Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summ…

eess.AS2017

Speaker Diarization with LSTM

Quan Wang, Carlton Downey, Li Wan +2

For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications. However, mirroring the rise of d…

eess.AS2017

Generalized End-to-End Loss for Speaker Verification

Li Wan, Quan Wang, Alan Papir +1

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 tu…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.