Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations
This work addresses speech emotion recognition for applications like human-computer interaction, but it is incremental as it builds on existing distillation and multimodal techniques.
The paper tackles speech emotion recognition by proposing EmoDistill, a framework that uses cross-modal knowledge distillation to learn prosodic and linguistic representations from speech, achieving state-of-the-art performance with 77.49% unweighted accuracy and 78.91% weighted accuracy on the IEMOCAP benchmark.
We propose EmoDistill, a novel speech emotion recognition (SER) framework that leverages cross-modal knowledge distillation during training to learn strong linguistic and prosodic representations of emotion from speech. During inference, our method only uses a stream of speech signals to perform unimodal SER thus reducing computation overhead and avoiding run-time transcription and prosodic feature extraction errors. During training, our method distills information at both embedding and logit levels from a pair of pre-trained Prosodic and Linguistic teachers that are fine-tuned for SER. Experiments on the IEMOCAP benchmark demonstrate that our method outperforms other unimodal and multimodal techniques by a considerable margin, and achieves state-of-the-art performance of 77.49% unweighted accuracy and 78.91% weighted accuracy. Detailed ablation studies demonstrate the impact of each component of our method.