End-to-End time-continuous emotion recognition for spontaneous interactions
Abschlussarbeit (Master; Diplom)
Authors
Kim, Bobae
Referee
Minker, WolfgangSchwenker, Friedhelm
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und PsychologieInstitutions
Institut für NachrichtentechnikAbstract
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).
Date created
2018
Subject Headings
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]
Keywords
Time-continuous emotion recognition; End-to-end modelling; Spectrogram; Speech emotion recognitionDewey Decimal Group
DDC 000 / Computer science, information & general worksDDC 410 / Linguistics
Metadata
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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