Datasets:

ag2003
/

bhavvani

Audio File stringlengths 13 17	Speaker stringclasses 20 values	Gender stringclasses 5 values	Final Annotation stringclasses 10 values
train_1000_1.mp4	null	M	Neutral
train_1000_2.mp4	Sahil	M	Surprise
train_1000_3.mp4	null	M	Enjoyment
train_1000_4.mp4	Roshesh	M	Neutral
train_1000_5.mp4	null	M	Enjoyment
train_1000_6.mp4	Sahil	M	Enjoyment
train_1001_0.mp4	Maya	F	Disgust
train_1001_1.mp4	Monisha	F	Sadness
train_1002_0.mp4	Monisha	F	Anger
train_1002_1.mp4	null	M	Fear
train_1002_2.mp4	Monisha	F	Anger
train_1003_0.mp4	Sahil	M	Enjoyment
train_1003_1.mp4	Monisha	F	Enjoyment
train_1003_2.mp4	Sahil	M	Anger
train_1004_0.mp4	Roshesh	M	Enjoyment
train_1004_1.mp4	Monisha	F	Enjoyment
train_1005_0.mp4	Sahil	M	Anger
train_1005_1.mp4	Roshesh	M	Neutral
train_1005_2.mp4	Sahil	M	Neutral
train_1005_3.mp4	Maya	F	Sadness
train_1006_0.mp4	Indra	M	Sadness
train_1006_1.mp4	Maya	F	Neutral
train_1006_2.mp4	Indra	M	Disgust
train_1007_0.mp4	Maya	F	Enjoyment
train_1007_1.mp4	Indra	M	Disgust
train_1008_0.mp4	Maya	F	Disgust
train_1009_0.mp4	Roshesh	M	Neutral
train_1009_1.mp4	Monisha	F	Neutral
train_1009_2.mp4	Maya	F	Enjoyment
train_1009_3.mp4	Monisha	F	Surprise
train_1009_4.mp4	Maya	F	Disgust
train_1009_5.mp4	Monisha	F	Disgust
train_1010_0.mp4	Maya	F	Disgust
train_1010_1.mp4	Indra	M	Enjoyment
train_1010_2.mp4	Maya	F	Disgust
train_1010_3.mp4	Indra	M	Enjoyment
train_1010_4.mp4	Maya	F	Disgust
train_1010_5.mp4	null	null	null
train_1010_6.mp4	Maya	F	Enjoyment
train_1011_0.mp4	Indra	M	Anger
train_1011_1.mp4	Maya	F	Neutral
train_1011_2.mp4	Indra	M	Disgust
train_1011_3.mp4	null	M	Disgust
train_1012_0.mp4	Monisha	F	Surprise
train_1012_1.mp4	null	null	null
train_1012_3.mp4	Sahil	M	Anger
train_1013_0.mp4	Indra	M	Enjoyment
train_1013_1.mp4	Roshesh	M	Enjoyment
train_1013_2.mp4	Maya	F	Disgust
train_1013_3.mp4	Roshesh	M	Enjoyment
train_1013_4.mp4	Indra	M	Anger
train_1014_0.mp4	Maya	F	Enjoyment
train_1014_2.mp4	Indra	M	Surprise
train_1014_3.mp4	Maya	F	Fear
train_1014_4.mp4	Sahil	M	Anger
train_1014_5.mp4	Maya	F	Fear
train_1014_6.mp4	Monisha	F	Enjoyment
train_1014_7.mp4	Indra	M	Anger
train_1015_0.mp4	Others	F	Enjoyment
train_1015_2.mp4	Maya	F	Anger
train_1015_3.mp4	Others	F	Anger
train_1016_0.mp4	Sahil	M	Disgust
train_1016_1.mp4	Monisha	F	Anger
train_1016_2.mp4	Sahil	M	Neutral
train_1016_3.mp4	Monisha	F	Anger
train_1016_4.mp4	Monisha	F	Sadness
train_1016_5.mp4	Sahil	M	Neutral
train_1017_0.mp4	Indra	M	Enjoyment
train_1017_1.mp4	Monisha	F	Sadness
train_1017_2.mp4	Indra	M	Enjoyment
train_1018_1.mp4	Maya	F	Enjoyment
train_1018_2.mp4	Indra	M	Enjoyment
train_1018_3.mp4	Maya	F	Surprise
train_1018_4.mp4	Indra	M	Enjoyment
train_1019_0.mp4	Maya	F	Neutral
train_1019_1.mp4	Indra	M	Enjoyment
train_1019_2.mp4	Indra	M	Enjoyment
train_1019_3.mp4	Indra	M	Enjoyment
train_1019_4.mp4	Indra	M	Enjoyment
train_1019_5.mp4	null	null	null
train_1019_6.mp4	null	null	null
train_1019_7.mp4	Maya	F	Surprise
train_1019_8.mp4	Maya	F	Surprise
train_1019_9.mp4	Indra	M	Enjoyment
train_1020_0.mp4	Sahil	M	Neutral
train_1020_1.mp4	Maya	F	Neutral
train_1020_2.mp4	Sahil	M	Enjoyment
train_1020_3.mp4	Indra	M	Anger
train_1020_4.mp4	Sahil	M	Enjoyment
train_1021_0.mp4	Maya	F	Enjoyment
train_1021_1.mp4	Roshesh	M	Neutral
train_1022_0.mp4	null	M	Enjoyment
train_1022_1.mp4	null	M	Enjoyment
train_1022_2.mp4	null	M	Enjoyment
train_1023_0.mp4	Roshesh	M	Enjoyment
train_1023_1.mp4	Sahil	M	Neutral
train_1024_0.mp4	Maya	F	Enjoyment
train_1024_1.mp4	Indra	M	Disgust
train_1025_0.mp4	Sahil	M	Enjoyment
train_1025_1.mp4	Monisha	F	Enjoyment

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Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning

This repository contains the BhavVani dataset introduced in the INTERSPEECH 2024 Paper : Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning

Please fill this form for accessing the audio files associated with the BhavVani dataset: Form Link

Overview

In our work, we propose the following contributions:

We introduce the CAMuLeNet architecture for generalizing emotion recognition architectures to unseen speaker distributions using co-attention on features and multi-task learning:
We introduce the first-ever Hindi SER dataset - BhavVani. The statistics for the same are shared below:

Citation

If our work was found helpful, please feel free to leave a star and cite our work using:

@inproceedings{goel24_interspeech,
  title     = {Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning},
  author    = {Arnav Goel and Medha Hira and Anubha Gupta},
  year      = {2024},
  booktitle = {Interspeech 2024},
  pages     = {2340--2344},
  doi       = {10.21437/Interspeech.2024-1820},
  issn      = {2958-1796},
}

Terms

Commercial and Academic Use: The database is made available for non-commercial research purposes only. Any commercial use of this data is forbidden.

Redistribution: The user may not distribute the database or parts of it to any third party.

Publications: The use of data for illustrative purposes in publications is allowed. Publications include both scientific papers and presentations for scientific and/or educational purposes. In these cases, the identity of the subjects should be protected (i.e., no release of identifiable information of subjects).

Warranty: The database comes without any warranty. In no event shall the provider be held responsible for any loss or damage caused by the use of this data.

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