Getting More for Less: Using Weak Labels and AV-Mixup for Robust Audio-Visual Speaker Verification ...

Distance Metric Learning (DML) has typically dominated the audio-visual speaker verification problem space, owing to strong performance in new and unseen classes. In our work, we explored multitask learning techniques to further enhance DML, and show that an auxiliary task with even weak labels can...

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Bibliographic Details
Main Authors: Selvakumar, Anith, Fashandi, Homa
Format: Article in Journal/Newspaper
Language:unknown
Published: arXiv 2023
Subjects:
Sound cs.SD
Computer Vision and Pattern Recognition cs.CV
Machine Learning cs.LG
Multimedia cs.MM
Audio and Speech Processing eess.AS
FOS: Computer and information sciences
FOS: Electrical engineering, electronic engineering, information engineering
DML
Online Access:https://dx.doi.org/10.48550/arxiv.2309.07115
https://arxiv.org/abs/2309.07115
Description
Summary:Distance Metric Learning (DML) has typically dominated the audio-visual speaker verification problem space, owing to strong performance in new and unseen classes. In our work, we explored multitask learning techniques to further enhance DML, and show that an auxiliary task with even weak labels can increase the quality of the learned speaker representation without increasing model complexity during inference. We also extend the Generalized End-to-End Loss (GE2E) to multimodal inputs and demonstrate that it can achieve competitive performance in an audio-visual space. Finally, we introduce AV-Mixup, a multimodal augmentation technique during training time that has shown to reduce speaker overfit. Our network achieves state of the art performance for speaker verification, reporting 0.244%, 0.252%, 0.441% Equal Error Rate (EER) on the VoxCeleb1-O/E/H test sets, which is to our knowledge, the best published results on VoxCeleb1-E and VoxCeleb1-H. ... : Accepted to INTERSPEECH 2024 ...

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