GMP-ATL: Gender-augmented Multi-scale Pseudo-label Enhanced Adaptive Transfer Learning for Speech Emotion Recognition via HuBERT
arxiv(2024)
摘要
The continuous evolution of pre-trained speech models has greatly advanced
Speech Emotion Recognition (SER). However, there is still potential for
enhancement in the performance of these methods. In this paper, we present
GMP-ATL (Gender-augmented Multi-scale Pseudo-label Adaptive Transfer Learning),
a novel HuBERT-based adaptive transfer learning framework for SER.
Specifically, GMP-ATL initially employs the pre-trained HuBERT, implementing
multi-task learning and multi-scale k-means clustering to acquire frame-level
gender-augmented multi-scale pseudo-labels. Then, to fully leverage both
obtained frame-level and utterance-level emotion labels, we incorporate model
retraining and fine-tuning methods to further optimize GMP-ATL. Experiments on
IEMOCAP show that our GMP-ATL achieves superior recognition performance, with a
WAR of 80.0% and a UAR of 82.0%, surpassing state-of-the-art unimodal SER
methods, while also yielding comparable results with multimodal SER approaches.
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