Speaker Normalization for Self-supervised Speech Emotion Recognition

• URL: http://arxiv.org/abs/2202.01252v1
• Date: Wed, 2 Feb 2022 19:30:47 GMT
• Title: Speaker Normalization for Self-supervised Speech Emotion Recognition
• Authors: Itai Gat, Hagai Aronowitz, Weizhong Zhu, Edmilson Morais, Ron Hoory
• Abstract summary: We propose a gradient-based adversary learning framework that learns a speech emotion recognition task while normalizing speaker characteristics from the feature representation. We demonstrate the efficacy of our method on both speaker-independent and speaker-dependent settings and obtain new state-of-the-art results on the challenging IEMOCAP dataset.
• Score: 16.044405846513495
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large speech emotion recognition datasets are hard to obtain, and small datasets may contain biases. Deep-net-based classifiers, in turn, are prone to exploit those biases and find shortcuts such as speaker characteristics. These shortcuts usually harm a model's ability to generalize. To address this challenge, we propose a gradient-based adversary learning framework that learns a speech emotion recognition task while normalizing speaker characteristics from the feature representation. We demonstrate the efficacy of our method on both speaker-independent and speaker-dependent settings and obtain new state-of-the-art results on the challenging IEMOCAP dataset.

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