Deep Functional Multiple Index Models for Speech Emotion Recognition
Innovative deep-learning architecture for speech emotion recognition using functional data models.
Accuracy Enhancement Method for Speech Emotion Recognition from Spectrogram Using Temporal Frequency Correlation and Positional Information Learning Through Knowledge Transfer
Proposing a method to improve speech emotion recognition accuracy by utilizing ViT and knowledge transfer to analyze frequency correlation and transfer positional information.
MSAC-SERNet: A Unified Framework for Speaker-Independent Speech Emotion Recognition
Investigating the reliability of SER methods and proposing a unified framework for speech emotion recognition.
EmoDistill: Speech Emotion Recognition Framework with Prosodic and Linguistic Representations
EmoDistillは、音声から感情の強力な言語的および韻律的表現を学習するための新しい音声感情認識（SER）フレームワークです。
EMO-SUPERB: An In-depth Look at Speech Emotion Recognition and the Development of EMO-SUPERB Benchmark
Speech emotion recognition is enhanced through the development of EMO-SUPERB, a benchmark fostering collaboration and open-source initiatives.
EmoDistill: Speech Emotion Recognition Framework
EmoDistill proposes a novel framework for speech emotion recognition that leverages cross-modal knowledge distillation to learn linguistic and prosodic representations from speech, achieving state-of-the-art performance.
EMO-SUPERB: Enhancing Speech Emotion Recognition with EMOtion Speech Universal PERformance Benchmark
The author introduces EMO-SUPERB to address key issues in Speech Emotion Recognition, such as reproducibility, data leakage, and leveraging typed descriptions for improved performance.
Analyzing Speech Emotion Recognition from Real Voice Messages
The author explores the effectiveness of SER models using real-world voice messages, highlighting the importance of combining expert and non-expert annotations for improved results.
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