Speech Emotion Recognition Leveraging OpenAI's Whisper Representations and Attentive Pooling Methods
This addresses the problem of limited datasets for speech emotion recognition, offering a lightweight alternative to larger models, though it is incremental in applying existing pre-trained models to this task.
This work tackled speech emotion recognition by using OpenAI's Whisper representations with attention-based pooling methods, achieving a state-of-the-art 2.47% improvement in unweighted accuracy on the Persian ShEMO dataset.
Speech Emotion Recognition (SER) research has faced limitations due to the lack of standard and sufficiently large datasets. Recent studies have leveraged pre-trained models to extract features for downstream tasks such as SER. This work explores the capabilities of Whisper, a pre-trained ASR system, in speech emotion recognition by proposing two attention-based pooling methods, Multi-head Attentive Average Pooling and QKV Pooling, designed to efficiently reduce the dimensionality of Whisper representations while preserving emotional features. We experiment on English and Persian, using the IEMOCAP and ShEMO datasets respectively, with Whisper Tiny and Small. Our multi-head QKV architecture achieves state-of-the-art results on the ShEMO dataset, with a 2.47% improvement in unweighted accuracy. We further compare the performance of different Whisper encoder layers and find that intermediate layers often perform better for SER on the Persian dataset, providing a lightweight and efficient alternative to much larger models such as HuBERT X-Large. Our findings highlight the potential of Whisper as a representation extractor for SER and demonstrate the effectiveness of attention-based pooling for dimension reduction.