emoDARTS: Joint Optimization of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
Optimizing joint CNN and SeqNN architectures using DARTS enhances SER performance.
emoDARTS: Joint Optimization of CNN & Sequential Neural Network Architectures for Superior Speech Emotion Recognition
Optimizing joint CNN and SeqNN architectures using DARTS enhances SER performance.
Vesper: A Compact and Effective Pretrained Model for Improving Speech Emotion Recognition
This paper proposes Vesper, a compact and effective pretrained model for speech emotion recognition, which is built upon the general pretrained model WavLM. Vesper employs an emotion-guided masking strategy and hierarchical and cross-layer self-supervision to enhance its sensitivity to emotional information and its ability to capture both acoustic and semantic representations, which are crucial for emotion recognition.
Enhancing Speech Emotion Recognition Through Multi-Spatial Fusion and Hierarchical Cooperative Attention
A novel method for Speech Emotion Recognition using Multi-Spatial Fusion and Hierarchical Cooperative Attention on spectrograms and raw audio to efficiently identify emotion-related regions and integrate higher-level acoustic information.
Active Learning with Task Adaptation Pre-training for Efficient and Robust Speech Emotion Recognition
The proposed AFTER framework leverages task adaptation pre-training and active learning to enhance the performance and efficiency of speech emotion recognition models, addressing the information gap, noise sensitivity, and low efficiency issues of existing methods.
Enhancing Speech Emotion Recognition through Gender-Augmented Multi-scale Pseudo-label Adaptive Transfer Learning with HuBERT
The proposed GMP-ATL framework leverages gender-augmented multi-scale pseudo-labels and adaptive transfer learning with the pre-trained HuBERT model to significantly improve speech emotion recognition performance.
Efficient Channel Attention and Data Augmentation Improve Speech Emotion Recognition Performance
Applying efficient channel attention (ECA) and data augmentation with different STFT preprocessing settings can significantly improve speech emotion recognition performance.
Temporally-Aware Bi-directional Dense Multi-Scale Network (TBDM-Net) for Accurate Speech Emotion Recognition
TBDM-Net, a novel deep neural network architecture, achieves state-of-the-art performance in speech emotion recognition across multiple multilingual datasets by leveraging temporally-aware bidirectional dense networks and multi-scale feature fusion.
The Impact of Perceptual Evaluations from Different Modalities on Speech Emotion Recognition System Performance
The choice of emotional labels elicited by different modalities (audio-only, facial-only, audio-visual) can significantly impact the performance of speech emotion recognition (SER) systems.
Exploring the Effectiveness of Large Language Models for Emotion Recognition in Post-ASR Speech Using Context and System Fusion
Leveraging large language models (LLMs) with carefully designed prompting strategies incorporating context and multiple ASR system outputs significantly improves post-ASR speech emotion recognition accuracy without task-specific training.
