Speech Emotion Recognition via Entropy-Aware Score Selection
This work addresses speech emotion recognition, a domain-specific problem, with an incremental improvement over existing methods.
The paper tackles speech emotion recognition by proposing a multimodal framework that combines acoustic and textual predictions using entropy-aware score selection, achieving practical and reliable enhancement over traditional single-modality systems on IEMOCAP and MSP-IMPROV datasets.
In this paper, we propose a multimodal framework for speech emotion recognition that leverages entropy-aware score selection to combine speech and textual predictions. The proposed method integrates a primary pipeline that consists of an acoustic model based on wav2vec2.0 and a secondary pipeline that consists of a sentiment analysis model using RoBERTa-XLM, with transcriptions generated via Whisper-large-v3. We propose a late score fusion approach based on entropy and varentropy thresholds to overcome the confidence constraints of primary pipeline predictions. A sentiment mapping strategy translates three sentiment categories into four target emotion classes, enabling coherent integration of multimodal predictions. The results on the IEMOCAP and MSP-IMPROV datasets show that the proposed method offers a practical and reliable enhancement over traditional single-modality systems.