Improving speaker verification robustness with synthetic emotional
  utterances
Authors: name: Nikhil Kumar Koditala, name: Chelsea Jui-Ting Ju, name: Ruirui Li, name: Minho Jin, name: Aman Chadha, name: Andreas Stolcke

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.