Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Multimodal Learning for Emotion Recognition in Spoken Language   In this paper, we present a novel deep multimodal framework to predict human
emotions based on sentence-level spoken language. Our architecture has two
distinctive characteristics. First, it extracts the high-level features from
both text and audio via a hybrid deep multimodal structure, which considers the
spatial information from text, temporal information from audio, and high-level
associations from low-level handcrafted features. Second, we fuse all features
by using a three-layer deep neural network to learn the correlations across
modalities and train the feature extraction and fusion modules together,
allowing optimal global fine-tuning of the entire structure. We evaluated the
proposed framework on the IEMOCAP dataset. Our result shows promising
performance, achieving 60.4% in weighted accuracy for five emotion categories.

21
The categories of this paper are: adap-org
16
The categories of this paper are: stat
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex