The hinterland of emotions: Facing the open-microphone challenge

We first depict the challenge to address all non-prototypical varieties of emotional states signalled in speech in an open microphone setting, i. e. using all data recorded. In the remainder of the article, we illustrate promising strategies, using the FAU Aibo emotion corpus, by showing different degrees of classification performance for different degrees of prototypicality, and by elaborating on the use of ROC curves, classification confidences, and the use of correlation-based analyses.

[1]  Elmar Nöth,et al.  Tales of tuning - prototyping for automatic classification of emotional user states , 2005, INTERSPEECH.

[2]  Stan Matwin,et al.  Discriminative parameter learning for Bayesian networks , 2008, ICML '08.

[3]  Björn W. Schuller,et al.  Patterns, prototypes, performance: classifying emotional user states , 2008, INTERSPEECH.

[4]  Elmar Nöth,et al.  Private emotions versus social interaction: a data-driven approach towards analysing emotion in speech , 2008, User Modeling and User-Adapted Interaction.

[5]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques, 3rd Edition , 1999 .

[6]  Björn W. Schuller,et al.  Speaker independent emotion recognition by early fusion of acoustic and linguistic features within ensembles , 2005, INTERSPEECH.

[7]  Stefan Steidl,et al.  Automatic classification of emotion related user states in spontaneous children's speech , 2009 .

[8]  Georg Stemmer.  Modeling variability in speech recognition , 2004 .

[9]  Björn Schuller,et al.  Being bored? Recognising natural interest by extensive audiovisual integration for real-life application , 2009, Image Vis. Comput..

[10]  Loïc Kessous,et al.  The relevance of feature type for the automatic classification of emotional user states: low level descriptors and functionals , 2007, INTERSPEECH.

[11]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[12]  Martin Fowler,et al.  Patterns , 2021, IEEE Software.

[13]  P. Shaver,et al.  Emotion knowledge: further exploration of a prototype approach. , 1987 .

[14]  Elmar Nöth,et al.  A Taxonomy of Applications that Utilize Emotional Awareness , 2006 .

[15]  J. Russell,et al.  Concept of Emotion Viewed From a Prototype Perspective , 1984 .

[16]  Björn W. Schuller,et al.  The INTERSPEECH 2009 emotion challenge , 2009, INTERSPEECH.

[17]  W. Stroebe,et al.  Introduction to social psychology: A European perspective , 1988 .

[18]  Björn W. Schuller,et al.  OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit , 2009, 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops.

[19]  P. Shaver,et al.  Emotion knowledge: further exploration of a prototype approach. , 1987, Journal of personality and social psychology.

[20]  Björn W. Schuller,et al.  Emotion recognition from speech: Putting ASR in the loop , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[21]  E. Rosch.  Cognitive Representations of Semantic Categories. , 1975 .

[22]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.