Disentangled Speaker And Nuisance Attribute Embedding For Robust Speaker Verification

Abstract

Over the recent years, various deep learning-based embedding methods have been proposed and have shown impressive performance in speaker verification. However, as in most of the classical embedding techniques, the deep learning-based methods are known to suffer from severe performance degradation when dealing with speech samples with different conditions (e.g., recording devices, emotional states). In this paper, we propose a novel fully supervised training method for extracting a speaker embedding vector disentangled from the variability caused by the nuisance attributes. The proposed framework was compared with the conventional deep learning-based embedding methods using the RSR2015 and VoxCeleb1 dataset. Experimental results show that the proposed approach can extract speaker embeddings robust to channel and emotional variability.

Stats

• citations13
• S2 citations—
• github stars0
• HF likes0
• heat score8.60
• arxiv keykang2020disentangled

Related papers

• A Joint Noise Disentanglement And Adversarial Training Framework For Robust Speaker Verification (2024)6.34
• DEAAN: Disentangled Embedding And Adversarial Adaptation Network For Robust Speaker Representation Learning (2020)9.59
• Disentangled Representation Learning For Environment-agnostic Speaker Recognition (2024)4.82
• Within-sample Variability-invariant Loss For Robust Speaker Recognition Under Noisy Environments (2020)11.85
• Feature Enhancement With Deep Feature Losses For Speaker Verification (2019)10.61
• Intra-class Variation Reduction Of Speaker Representation In Disentanglement Framework (2020)8.35
• Deep Speaker Embeddings For Far-field Speaker Recognition On Short Utterances (2020)11.29
• Leveraging Speaker Attribute Information Using Multi Task Learning For Speaker Verification And Diarization (2020)6.34