What Does it Take to Generalize SER Model Across Datasets? A Comprehensive Benchmark

Autor:	Ibrahim, Adham, Shehata, Shady, Kulkarni, Ajinkya, Mohamed, Mukhtar, Abdul-Mageed, Muhammad
Rok vydání:	2024
Předmět:	Computer Science - Sound Computer Science - Artificial Intelligence Computer Science - Human-Computer Interaction Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Speech emotion recognition (SER) is essential for enhancing human-computer interaction in speech-based applications. Despite improvements in specific emotional datasets, there is still a research gap in SER's capability to generalize across real-world situations. In this paper, we investigate approaches to generalize the SER system across different emotion datasets. In particular, we incorporate 11 emotional speech datasets and illustrate a comprehensive benchmark on the SER task. We also address the challenge of imbalanced data distribution using over-sampling methods when combining SER datasets for training. Furthermore, we explore various evaluation protocols for adeptness in the generalization of SER. Building on this, we explore the potential of Whisper for SER, emphasizing the importance of thorough evaluation. Our approach is designed to advance SER technology by integrating speaker-independent methods. Comment: ACCEPTED AT INTERSPEECH 2024, GREECE
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2406.09933 Zobrazit plný text záznamu View this record from Arxiv