Work

Data-driven clustering in emotional space for affect recognition using discriminatively trained LSTM networks

Year: 2009

Type: conference-paper

Abstract: In today's affective databases speech turns are often labelled on a continuous scale for emotional dimensions such as valence or arousal to better express the diversity of human affect.However, applications like virtual agents usually map the detected emotional user state to rough classes in order to reduce the multiplicity of emotion dependent system responses.Since these classes often do not optimally reflect emotions that typically occur in a given application, this paper investigates data-driven clustering of emotional space to find class divisions that better match the training data and the area of application.Thereby we consider the Belfast Sensitive Artificial Listener database and TV talkshow data from the VAM corpus.We show that a discriminatively trained Long Short-Term Memory (LSTM) recurrent neural net that explicitly learns clusters in emotional space and additionally models context information outperforms both, Support Vector Machines and a Regression-LSTM net.

Source: Interspeech (USB)

DOI: 10.21437/interspeech.2009-474

Authors: Martin Wöllmer, Florian Eyben, Björn Schuller, Ellen Douglas-Cowie, Roddy Cowie

Institution: Technical University of Munich

Language: English

Topic: Emotion and Mood Recognition

Subfield: Experimental and Cognitive Psychology

Field: Psychology

Domain: Social Sciences

Open Access status: green

Funder: European Commission

Award: Sustained Emotionally coloured Machine-human Interaction using Nonverbal Expression