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Detail publikačního výsledku
HARÁR, P.; BURGET, R.; DUTTA, M.; SINGH, A.
Original Title
Speech Emotion Recognition with Deep Learning
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
This paper describes a method for Speech Emotion Recognition (SER) using Deep Neural Network (DNN) architecture with convolutional, pooling and fully connected layers. We used 3 class subset (angry, neutral, sad) of German Corpus (Berlin Database of Emotional Speech) containing 271 labeled recordings with total length of 783 seconds. Raw audio data were standardized so every audio file has zero mean and unit variance. Every file was split into 20 millisecond segments without overlap. We used Voice Activity Detection (VAD) algorithm to eliminate silent segments and divided all data into TRAIN (80%) VALIDATION (10%) and TESTING (10%) sets. DNN is optimized using Stochastic Gradient Descent. As input we used raw data without any feature selection. Our trained model achieved overall test accuracy of 96.97% on whole-file classification.
English abstract
Keywords
Emotion; Speech Recognition; Deep Learning; Classification
Key words in English
Authors
RIV year
2018
Released
02.02.2017
Location
Noida, India
ISBN
978-1-5090-2796-5
Book
2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)
Pages from
137
Pages to
140
Pages count
4
URL
https://ieeexplore.ieee.org/document/8049931
Full text in the Digital Library
http://hdl.handle.net/
BibTex
@inproceedings{BUT133621, author="Pavol {Harár} and Radim {Burget} and Malay Kishore {Dutta} and Anushikha {Singh}", title="Speech Emotion Recognition with Deep Learning", booktitle="2017 4th International Conference on Signal Processing and Integrated Networks (SPIN)", year="2017", pages="137--140", address="Noida, India", doi="10.1109/SPIN.2017.8049931", isbn="978-1-5090-2796-5", url="https://ieeexplore.ieee.org/document/8049931" }