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End-to-End time-continuous emotion recognition for spontaneous interactions

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BobaeKimThesis.pdf (1.634Mb)
Erstveröffentlichung
2019-08-07
DOI
10.18725/OPARU-17725
Abschlussarbeit (Master; Diplom)


Autoren
Kim, Bobae
Gutachter
Minker, Wolfgang
Schwenker, Friedhelm
Fakultäten
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Institutionen
Institut für Nachrichtentechnik
Lizenz
Standard
https://oparu.uni-ulm.de/xmlui/license_v3
Zusammenfassung
Speech emotion recognition has emerged in the area of speech signal research since it can have a signficant role in Artificial intelligent. In the area of speech emotion recognition, hand-engineered features are traditionally used as an input. However, it requires an additional step to extract features before the prediction and prior knowledge to select a feature set. For this reason, recent research is focusing on end-to-end speech emotion recognition to reduce the required efforts for the feature extraction and increase performance. Whereas this approach has been applied for prediction of categorical emotions, the study for prediction of continuous dimensional emotions is still rare. This paper presents a method for time-continuous prediction of emotions from speech in end-to-end manner. Proposed model comprises convolutional neural network (CNN) and Recurrent Neural Network with Long Short-Term Memory (RNN-LSTM). Hyperparameters of CNN are investigated to improve the performance of our model. After finding the optimal hyperparameters, the performance of the system with waveform and spectrogram as input is compared in terms of concordance correlation coefficient (CCC).
Erstellung / Fertigstellung
2018
Normierte Schlagwörter
Ende-zu-Ende-Prinzip [GND]
Sonogramm [GND]
Schallaufzeichnung [GND]
Emotion recognition [LCSH]
Speech perception [LCSH]
Machine learning [LCSH]
Artificial intelligence [LCSH]
Speech recognition software [MeSH]
Deep learning [MeSH]
Schlagwörter
Time-continuous emotion recognition; End-to-end modelling; Spectrogram; Speech emotion recognition
DDC-Sachgruppe
DDC 000 / Computer science, information & general works
DDC 410 / Linguistics

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Zitiervorlage

Kim, Bobae (2019): End-to-End time-continuous emotion recognition for spontaneous interactions. Open Access Repositorium der Universität Ulm. http://dx.doi.org/10.18725/OPARU-17725

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