Large Raw Emotional Dataset with Aggregation Mechanism (2022-12-23T00:00:00.000000Z)

TL;DR

This paper describes pre-processing routine, annotation, and experiment with a baseline model to demonstrate some actual metrics which could be obtained with the Dusha data set.

Abstract

We present a new data set for speech emotion recognition (SER) tasks called Dusha. The corpus contains approximately 350 hours of data, more than 300 000 audio recordings with Russian speech and their transcripts. Therefore it is the biggest open bi-modal data collection for SER task nowadays. It is annotated using a crowd-sourcing platform and includes two subsets: acted and real-life. Acted subset has a more balanced class distribution than the unbalanced real-life part consisting of audio podcasts. So the first one is suitable for model pre-training, and the second is elaborated for fine-tuning purposes, model approbation, and validation. This paper describes pre-processing routine, annotation, and experiment with a baseline model to demonstrate some actual metrics which could be obtained with the Dusha data set.

Authors

V. Kondratenko

1 papers

N. Karpov

2 papers

O. Kutuzov

1 papers

Large Raw Emotional Dataset with Aggregation Mechanism

TL;DR

Abstract

Authors

References22 items

Golos: Russian Dataset for Speech Research

EmoNet: A Transfer Learning Framework for Multi-Corpus Speech Emotion Recognition

Multimodal Russian Corpus and its use in emotional studies

MLS: A Large-Scale Multilingual Dataset for Speech Research

An Efficient Temporal Modeling Approach for Speech Emotion Recognition by Mapping Varied Duration Sentences into Fixed Number of Chunks

A Cross-Corpus Study on Speech Emotion Recognition

RAMAS: Russian Multimodal Corpus of Dyadic Interaction for Affective Computing

Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph

Self-Attention Generative Adversarial Networks

End-to-End Speech Emotion Recognition Using Deep Neural Networks

Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation

MOSI: Multimodal Corpus of Sentiment Intensity and Subjectivity Analysis in Online Opinion Videos

Adam: A Method for Stochastic Optimization

IEMOCAP: interactive emotional dyadic motion capture database

Audiovisual Behavior Modeling by Combined Feature Spaces

2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection

RUSLANA: a database of Russian emotional utterances

The SmartKom Multimodal Corpus at BAS

Maximum Likelihood Estimation of Observer Error‐Rates Using the EM Algorithm

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

A database of German emotional speech

Schuller , “ Speech emotion recognition : Two decades in a nutshell , benchmarks , and ongoing trends

Field of Study