Lightweight speech emotion recognition: integrating acoustic-aware augmentation and confusion matrix-guided adaptive training

ASPIRE-SER: An Adaptive Acoustic Transformer Graph Framework with Calibrated Selective Prediction for Reliable Speech Emotion Recognition

An encoder–decoder transformer with signal-level data augmentation for robust speech emotion recognition

Zero-shot emotional speech synthesis based on feature decoupling and adaptive loss-threshold reweighting

Deep learning-based zero-shot speech synthesis has achieved substantial progress in speaker generalization, but stable modeling remains challenging in fine-grained emotional scenarios. Existing systems often process textual and emotional conditions through shared or closely coupled pathways, which may introduce interfe...

Emotion as a Distribution: Joint Valence-Arousal Probability Learning for Speaker-Independent Multimodal Emotion Recognition

This text+speech system, alongside its categorical decision, emits a $9\times9$ probability matrix over the Valence-Arousal plane, trained with a two-dimensional Gaussian soft target under a Kullback-Leibler/cross-entropy objective, aimed at counseling support.

ASER: Attribute-Serialized Emotion Recognition With Generative Multi-Task Learning

Speech emotion recognition (SER) is commonly formulated as direct utterance-to-label classification, leaving the acoustic and semantic evidence behind each prediction implicit. This letter proposes Attribute-Serialized Emotion Recognition (ASER), an attribute-serialized generative multi-task framework for SER, which or...

Hybrid CNN-embedding fusion with MFCC-SVM for speech emotion recognition: Random vs actor-wise evaluation on CREMA-D

The results show that a controlled lightweight fusion framework can improve robustness while making the performance gap between speaker-overlapping and speaker-independent evaluation explicit, and improve robustness while making the performance gap between speaker-overlapping and speaker-independent evaluation explicit...