Core Concepts
Investigating the reliability of SER methods and proposing a unified framework for speech emotion recognition.
Abstract
The content introduces MSAC-SERNet, a novel framework for Speaker-Independent Speech Emotion Recognition. It focuses on the reliability of SER methods in the presence of semantic data shifts and explores fine-grained control over speech attributes. The framework outperforms existing approaches in both single-corpus and cross-corpus scenarios.
Structure:
- Introduction to Speech Emotion Recognition (SER)
- Challenges in SER and Existing Approaches
- Proposed MSAC-SERNet Framework Overview
- Detailed Methodology: Input Pipeline, Feature Extraction, Aggregation Pooling, Loss Function, Multiple Speech Attribute Control Method
- Experimental Databases and Setup: Datasets Used, Implementation Details, Evaluation Metrics
- Experimental Results and Discussion: Comparison with Existing Works, Ablation Study, Reliability Comparison and Analysis
- Conclusions and Future Work
Stats
"Our proposed MSAC approach enables the proposed base SER model to achieve the highest reliability performance."
"When incorporating the proposed MSAC learning paradigm, the proposed SER model obtains a 5.98% reduction in FPR95."
"The proposed rODIN method attains the best reliability performance as well."