Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Jointly Fine-Tuning "BERT-like" Self Supervised Models to Improve
  Multimodal Speech Emotion Recognition   Multimodal emotion recognition from speech is an important area in affective
computing. Fusing multiple data modalities and learning representations with
limited amounts of labeled data is a challenging task. In this paper, we
explore the use of modality-specific "BERT-like" pretrained Self Supervised
Learning (SSL) architectures to represent both speech and text modalities for
the task of multimodal speech emotion recognition. By conducting experiments on
three publicly available datasets (IEMOCAP, CMU-MOSEI, and CMU-MOSI), we show
that jointly fine-tuning "BERT-like" SSL architectures achieve state-of-the-art
(SOTA) results. We also evaluate two methods of fusing speech and text
modalities and show that a simple fusion mechanism can outperform more complex
ones when using SSL models that have similar architectural properties to BERT.

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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
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The categories of this paper are: hep-ex