Speech Emotion Recognition using Self-Supervised Features

• URL: http://arxiv.org/abs/2202.03896v1
• Date: Mon, 7 Feb 2022 00:50:07 GMT
• Title: Speech Emotion Recognition using Self-Supervised Features
• Authors: Edmilson Morais, Ron Hoory, Weizhong Zhu, Itai Gat, Matheus Damasceno and Hagai Aronowitz
• Abstract summary: We introduce a modular End-to- End (E2E) SER system based on an Upstream + Downstream architecture paradigm. Several SER experiments for predicting categorical emotion classes from the IEMOCAP dataset are performed. The proposed monomodal speechonly based system achieves SOTA results, but also brings light to the possibility of powerful and well finetuned self-supervised acoustic features.
• Score: 14.954994969217998
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Self-supervised pre-trained features have consistently delivered state-of-art results in the field of natural language processing (NLP); however, their merits in the field of speech emotion recognition (SER) still need further investigation. In this paper we introduce a modular End-to- End (E2E) SER system based on an Upstream + Downstream architecture paradigm, which allows easy use/integration of a large variety of self-supervised features. Several SER experiments for predicting categorical emotion classes from the IEMOCAP dataset are performed. These experiments investigate interactions among fine-tuning of self-supervised feature models, aggregation of frame-level features into utterance-level features and back-end classification networks. The proposed monomodal speechonly based system not only achieves SOTA results, but also brings light to the possibility of powerful and well finetuned self-supervised acoustic features that reach results similar to the results achieved by SOTA multimodal systems using both Speech and Text modalities.

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