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The paper proposes a discriminative adaptation of SpeechLLMs for emotion recognition, improving performance and interpretability by using a linear classification head on the hidden state of the final prompt token, which removes hallucinations and enhances analysis of emotion directions.
This paper studies using Whisper for Persian speech emotion recognition, showing PCA-based dimensionality reduction improves performance and efficiency, while ASR fine-tuning offers only modest gains.
This paper introduces AMRD, an adaptive multi-teacher relational distillation method for compressing large self-supervised speech emotion recognition models into lightweight student models for edge devices. It addresses teacher reliability variation and relational structure loss, showing improvements on IEMOCAP and CREMA-D datasets.