Improving speaker verification robustness with synthetic emotional utterances
Nikhil Kumar Koditala, Chelsea Jui-Ting Ju, Ruirui Li, Minho Jin, Aman Chadha, Andreas Stolcke
2024-12-03
Summary
This paper discusses a method to improve speaker verification systems by using synthetic emotional speech to enhance their ability to recognize speakers, even when they express different emotions.
What's the problem?
Speaker verification systems are designed to confirm whether a voice belongs to a specific person. However, these systems often struggle when the speaker shows emotions like happiness or anger, leading to mistakes in recognizing them. This is mainly because there isn't enough emotional speech data available for training these systems, making it difficult for them to learn how to handle various emotional tones effectively.
What's the solution?
To solve this problem, the researchers used a technique called CycleGAN to create synthetic emotional speech samples for each speaker. This method generates emotional versions of neutral speech while keeping the speaker's unique voice characteristics intact. By training speaker verification models with both real and synthetic emotional data, they found that the models performed better in recognizing speakers during emotional situations, reducing errors by up to 3.64%.
Why it matters?
This research is important because it helps make speaker verification systems more reliable and inclusive by considering the emotional aspects of human speech. By improving how these systems recognize voices under different emotional conditions, we can enhance security and accessibility in applications like voice-activated devices and personal assistants, ensuring they work well for everyone regardless of their emotional state.
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 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.