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 …

Date

2024

Authors

Tiantian Feng, Rajat Hebbar, Shrikanth Narayanan

Conference

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

Pages

11201-11205

Publisher

IEEE