  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.

Audio and Speech Processing