Abstract

Recent studies have explored using pre-trained embeddings for speech emotion recognition, achieving comparable performance to conventional methods that rely on low-level knowledge-inspired acoustic features. These embeddings are often generated from models trained on large-scale speech datasets using self-supervised or weakly-supervised learning objectives. Despite the significant advancements made in SER through pre-trained embeddings, there is a limited understanding of the trustworthiness of these methods, including privacy breaches, unfair performance, vulnerability to adversarial attacks, and computational cost, all of which may hinder the real-world deployment of these systems. In response, we introduce TrustSER, a general framework designed to evaluate the trustworthiness of SER systems using deep learning methods, focusing on privacy, safety, fairness, and sustainability, offering …

Metadata

publication

ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024

year

2024

publication date

2024/4/14

authors

Tiantian Feng, Rajat Hebbar, Shrikanth Narayanan

link

https://ieeexplore.ieee.org/abstract/document/10446616/

resource_link

https://arxiv.org/pdf/2305.11229

conference

ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

pages

11201-11205

publisher

IEEE