Improving speaker verification robustness with synthetic emotional
  utterances
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 unique
vocal identity. Our experimental findings underscore the effectiveness of
incorporating synthetic emotional data into the training process. The models
trained using this augmented dataset consistently outperform the baseline
models on the task of verifying speakers in emotional speech scenarios,
reducing equal error rate by as much as 3.64% relative.
674f607137be5cc99d9ceaae
Nikhil Kumar Koditala
674f607137be5cc99d9ceaaf
Chelsea Jui-Ting Ju
674f607137be5cc99d9ceab0
Ruirui Li
674f607137be5cc99d9ceab1
Minho Jin
674f607137be5cc99d9ceab2
Aman Chadha
claimed_verified
2024-12-04T09:46:11.731Z
63a4754927f1f64ed7238dac
https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
Aman Chadha
user
amanchadha
674f607137be5cc99d9ceab3
Andreas Stolcke
Improving speaker verification robustness with synthetic emotional
  utterances
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 unique
vocal identity. Our experimental findings underscore the effectiveness of
incorporating synthetic emotional data into the training process. The models
trained using this augmented dataset consistently outperform the baseline
models on the task of verifying speakers in emotional speech scenarios,
reducing equal error rate by as much as 3.64% relative.
A CycleGAN-based data augmentation method improves speaker verification performance in emotional speech by reducing the equal error rate.
CycleGAN data augmentation emotional speech speaker verification equal error rate