title: Improving speaker verification robustness with synthetic emotional
    utterances
summary: 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.
paper_authors._id: 674f607137be5cc99d9ceaae
paper_authors.name: Nikhil Kumar Koditala
paper_authors._id: 674f607137be5cc99d9ceaaf
paper_authors.name: Chelsea Jui-Ting Ju
paper_authors._id: 674f607137be5cc99d9ceab0
paper_authors.name: Ruirui Li
paper_authors._id: 674f607137be5cc99d9ceab1
paper_authors.name: Minho Jin
paper_authors._id: 674f607137be5cc99d9ceab2
paper_authors.name: Aman Chadha
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-12-04T09:46:11.731Z
paper_authors._id: 63a4754927f1f64ed7238dac
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63a4754927f1f64ed7238dac/aH-eJF-31g4vof9jv2gmI.jpeg
paper_authors.fullname: Aman Chadha
paper_authors.type: user
paper_authors.user: amanchadha
paper_authors._id: 674f607137be5cc99d9ceab3
paper_authors.name: Andreas Stolcke
paper_title: Improving speaker verification robustness with synthetic emotional
    utterances
paper_summary: 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.
paper_ai_keywords: CycleGAN data augmentation emotional speech segments speaker verification (SV) baseline models equal error rate