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Title: Personalized Adaptation with Pre-trained Speech Encoders for Continuous Emotion Recognition
There are individual differences in expressive behaviors driven by cultural norms and personality. This between-person variation can result in reduced emotion recognition performance. Therefore, personalization is an important step in improving the generalization and robustness of speech emotion recognition. In this paper, to achieve unsupervised personalized emotion recognition, we first pre-train an encoder with learnable speaker embeddings in a self-supervised manner to learn robust speech representations conditioned on speakers. Second, we propose an unsupervised method to compensate for the label distribution shifts by finding similar speakers and leveraging their label distributions from the training set. Extensive experimental results on the MSP-Podcast corpus indicate that our method consistently outperforms strong personalization baselines and achieves state-of-the-art performance for valence estimation.  more » « less
Award ID(s):
2211550
PAR ID:
10474297
Author(s) / Creator(s):
; ;
Publisher / Repository:
ISCA
Date Published:
Journal Name:
Proc. INTERSPEECH 2023
Page Range / eLocation ID:
636-640
Format(s):
Medium: X
Location:
Dublin, Ireland
Sponsoring Org:
National Science Foundation
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