Improving Speaker Verification Robustness With Synthetic Emotional Utterances

Abstract

A speaker verification (SV) system offers an authentication service designed to confirm whether a given speech sample originates from a specific speaker. This technology has paved the way for various personalized applications that cater to individual preferences. A noteworthy challenge faced by SV systems is their ability to perform consistently across a range of emotional spectra. Most existing models exhibit high error rates when dealing with emotional utterances compared to neutral ones. Consequently, this phenomenon often leads to missing out on speech of interest. This issue primarily stems from the limited availability of labeled emotional speech data, impeding the development of robust speaker representations that encompass diverse emotional states. To address this concern, we propose a novel approach employing the CycleGAN framework to serve as a data augmentation method. This technique synthesizes emotional speech segments for each specific speaker while preserving the uniqu

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keykoditala2024improving

Related papers

• Three-stage Speaker Verification Architecture In Emotional Talking Environments (2018)7.16
• Speaker Verification In Emotional Talking Environments Based On Three-stage Framework (2018)6.34
• On Enhancing Speech Emotion Recognition Using Generative Adversarial Networks (2018)12.33
• Augmenting Generative Adversarial Networks For Speech Emotion Recognition (2020)10.85
• Generative Emotional AI For Speech Emotion Recognition: The Case For Synthetic Emotional Speech Augmentation (2023)11.19
• Speaker Verification In Multi-speaker Environments Using Temporal Feature Fusion (2022)0.00
• An Improved Stargan For Emotional Voice Conversion: Enhancing Voice Quality And Data Augmentation (2021)7.81
• Why Disentanglement-based Speaker Anonymization Systems Fail At Preserving Emotions? (2025)3.58