Abstract

Significant advances are being made in speech emotion recognition (SER) using deep learning models. Nonetheless, training SER systems remains challenging, requiring both time and costly resources. Like many other machine learning tasks, acquiring datasets for SER requires substantial data annotation efforts, including transcription and labeling. These annotation processes present challenges when attempting to scale up conventional SER systems. Recent developments in foundational models have had a tremendous impact, giving rise to applications such as ChatGPT. These models have enhanced human-computer interactions including bringing unique possibilities for streamlining data collection in fields like SER. In this research, we explore the use of foundational models to assist in automating SER from transcription and annotation to augmentation. Our study demonstrates that these models can …

Metadata

publication

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

year

2024

publication date

2024/4/14

authors

Tiantian Feng, Shrikanth Narayanan

link

https://ieeexplore.ieee.org/abstract/document/10448130/

resource_link

https://arxiv.org/pdf/2309.08108

conference

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

pages

12116-12120

publisher

IEEE