Hyperbolic Additive Margin Softmax with Hierarchical Information for Speaker Verification

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References (30)

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Using gender, phonation and age to interpret automatically discovered speech attributes for explainable speaker recognition
2025Carole Millot, Clara Ponchard et al.
[2]
Learning Structured Representations with Hyperbolic Embeddings
2024Aditya Sinha, Siqi Zeng et al.
[3]
Deep Noise-Aware Quality Loss for Speaker Verification
2024Pantid Chantangphol, Theerat Sakdejayont et al.
[4]
Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning
2024Shuai Wang, Zheng-Shou Chen et al.
[5]
Multi-View Speaker Embedding Learning for Enhanced Stability and Discriminability
2024Liang He, Zhihua Fang et al.
[6]
Hyperbolic Distance-Based Speech Separation
2024Darius Petermann, Minje Kim
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Hyperbolic Representation Learning: Revisiting and Advancing
2023Menglin Yang, Min Zhou et al.
[8]
Adaptive Large Margin Fine-Tuning For Robust Speaker Verification
2023Leying Zhang, Zhengyang Chen et al.
[9]
Exploring Binary Classification Loss for Speaker Verification
2023Bing Han, Zhengyang Chen et al.
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Discriminative Speaker Representation Via Contrastive Learning with Class-Aware Attention in Angular Space
2022Zhe Li, M. Mak et al.
[11]
Lightweight Speaker Recognition in Poincaré Spaces
2022Jieun Lee, Kim Sung-Bin et al.
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Real Additive Margin Softmax for Speaker Verification
2021Lantian Li, Ruiqian Nai et al.
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SphereFace2: Binary Classification is All You Need for Deep Face Recognition
2021Yandong Wen, Weiyang Liu et al.
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Adaptive Margin Circle Loss for Speaker Verification
2021Runqiu Xiao
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CN-Celeb: multi-genre speaker recognition
2020Lantian Li, Ruiqi Liu et al.
[16]
Dynamic Margin Softmax Loss for Speaker Verification
2020Dao Zhou, Longbiao Wang et al.
[17]
ECAPA-TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification
2020Brecht Desplanques, Jenthe Thienpondt et al.
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Ensemble Additive Margin Softmax for Speaker Verification
2019Ya-Qi Yu, Lei Fan et al.
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Large Margin Softmax Loss for Speaker Verification
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Hyperbolic Image Embeddings
2019Valentin Khrulkov, L. Mirvakhabova et al.

Showing 20 of 30 references

Founder's Pitch

"Develop hyperbolic-based softmax models to improve hierarchical speaker verification accuracy."

Speaker VerificationScore: 6View PDF ↗

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0-10 scale

High Potential

2/4 signals

5

Quick Build

4/4 signals

10

Series A Potential

1/4 signals

2.5

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