@misc{Tumanyan_Narek_T._Deep,
 author={Tumanyan, Narek T.},
 howpublished={online},
 publisher={Изд-во НАН РА},
 language={en},
 abstract={In this paper, we present deep learning-based approaches for the task of emotionrecognition in voice recordings. A key component of the methods is the representationof emotion categories in a sentiment-arousal space and the usage of this space repre-sentation in the supervision signal. Our methods use wavelet and cepstral features asefficient data representations of audio signals. Convolutional Neural Network (CNN)and Long Short Term Memory Network (LSTM) architectures were used in recognitiontasks, depending on whether the audio representation was treated as a spatial signal oras a temporal signal. Various recognition approaches were used, and the results were analyzed․},
 title={Deep Learning Approaches for Voice Emotion Recognition Using Sentiment-Arousal Space},
 type={Հոդված},
 keywords={Mathematical cybernetics, Computer science},
}